Paper Digest: SIGIR 2020 Highlights

July 24, 2020August 18, 2020 admin

Download SIGIR-2020-Paper-Digests.pdf– highlights of all SIGIR-2020 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords and find related papers and patents.

SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top information retrieval conferences in the world. In 2020, the conference will be held online due to Covid-19 pandemic.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest service to get updates on new papers published in your area every day. You are also welcome to follow us on Twitter and Linkedin to get updated with new conference digests.

Paper Digest Team
team@paperdigest.org

TABLE 1: SIGIR 2020 Keynotes, Papers, Tutorials, Demos, etc.

	Title	Authors	Highlight
1	The Next Generation of Neural Networks	Geoffrey Hinton	I will describe a novel and effective way of dealing with this limitation.
2	Proof by Experimentation? Towards Better IR Research	Norbert Fuhr	Proof by Experimentation? Towards Better IR Research
3	Coopetition in IR Research	Ellen M. Voorhees	This talk will use TREC tracks as case studies to explore the benefits and disadvantages of different evaluation task designs.
4	On Presuppositions of Machine Learning: A Meta Theory	Zongben Xu	We report, in this presentation, the effort and advances made by my group on how to break through these presuppositions of ML and drive ML development.
5	How Deep Learning Works for Information Retrieval	Dacheng Tao	How Deep Learning Works for Information Retrieval
6	From Information to Assistance	Elizabeth F. Churchill	In this talk, I will focus on how people find information, and how the tools we build aid in that finding.
7	The New TREC Track on Podcast Search and Summarization	Rosie Jones	In this talk I will describe the task and dataset, outlining how the dataset is orders of magnitude larger than previous spoken document datasets, and how the tasks take us beyond previous shared tasks both in spoken document retrieval and NLP.
8	Large-scale Multi-modal Search and QA at Alibaba	Rong Jin	In this talk, we will present ongoing efforts of Alibaba on large-scale multi-modal search ad question-answering, including image search, video search, cross lingual retrieval, and QA based on machine reading and comprehension (MRC).
9	Training Effective Neural CLIR by Bridging the Translation Gap	Hamed Bonab, Sheikh Muhammad Sarwar, James Allan	We introduce Smart Shuffling, a cross-lingual embedding (CLE) method that draws from statistical word alignment approaches to leverage dictionaries, producing dense representations that are significantly more effective for cross-language information retrieval (CLIR) than prior CLE methods.
10	A Quantum Interference Inspired Neural Matching Model for Ad-hoc Retrieval	Yongyu Jiang, Peng Zhang, Hui Gao, Dawei Song	In our work, we propose a Quantum Interference inspired Neural Matching model (QINM), which can apply the interference effects to guide the construction of additional evidence generated by the interaction between matching units in the retrieval process.
11	A Deep Recurrent Survival Model for Unbiased Ranking	Jiarui Jin, Yuchen Fang, Weinan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai	In this paper, we propose an end-to-end Deep Recurrent Survival Ranking (DRSR), a unified framework to jointly model user’s various behaviors, to (i) consider the rich contextual information in the ranking list; and (ii) address the hidden issues underlying user behaviors, i.e., to mine observe pattern in queries without any click (non-click queries), and to model tracking logs which cannot truly reflect the user browsing intents (untrusted observation).
12	ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT	Omar Khattab, Matei Zaharia	To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval.
13	Efficient Document Re-Ranking for Transformers by Precomputing Term Representations	Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder	Our proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer networks (up to a 42x speedup on web document ranking) making these networks more practical to use in a real-time ranking scenario.
14	A Reinforcement Learning Framework for Relevance Feedback	Ali Montazeralghaem, Hamed Zamani, James Allan	We present RML, the first known general reinforcement learning framework for relevance feedback that directly optimizes any desired retrieval metric, including precision-oriented, recall-oriented, and even diversity metrics: RML can be easily extended to directly optimize any arbitrary user satisfaction signal.
15	Fairness-Aware Explainable Recommendation over Knowledge Graphs	Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, Gerard de Melo	In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups.
16	Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View	Jibing Gong, Shen Wang, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu	To learn the parameters of the proposed model, we propose to utilize extended matrix factorization (MF).
17	Sequential Recommendation with Self-Attentive Multi-Adversarial Network	Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, Ji-Rong Wen	In this paper, we present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation.
18	MVIN: Learning Multiview Items for Recommendation	Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, Lun-Wei Ku	To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model that provides superior recommendations by describing items from a unique mixed view from user and entity angles.
19	Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation	Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma	To model dynamic meanings of an item in different sequence contexts, a novel method Chorus is proposed to take both item relations and corresponding temporal dynamics into consideration.
20	Evolutionary Product Description Generation: A Dynamic Fine-Tuning Approach Leveraging User Click Behavior	Yongzhen Wang, Jian Wang, Heng Huang, Hongsong Li, Xiaozhong Liu	In this paper, our goal is to generate product descriptions on e-commerce platforms, and we explore this classic task from a novel perspective that allows the optimal output text to vary with ever-changing user preference.
21	Pairwise View Weighted Graph Network for View-based 3D Model Retrieval	Zan Gao, Yin-ming Li, Wei-li Guan, Wei-zhi Nie, Zhi-yong Cheng, An-an Liu	To obtain a more efficient feature descriptor for 3D model retrieval, in this work, we propose the pairwise view weighted graph network (abbreviated PVWGN) for view-based 3D model retrieval where non-local graph layers are embedded into the network architecture to automatically mine the intrinsic relationship among multiple views of a 3D model.
22	Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning	Zheng Gao, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu	In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities.
23	BiANE: Bipartite Attributed Network Embedding	Wentao Huang, Yuchen Li, Yuan Fang, Ju Fan, Hongxia Yang	In this paper, we propose a novel model called BiANE, short forBipartite Attributed Network Embedding.
24	Hierarchical Fashion Graph Network for Personalized Outfit Recommendation	Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua	In this work, we unify two tasks, fashion compatibility modeling and personalized outfit recommendation.
25	Global Context Enhanced Graph Neural Networks for Session-based Recommendation	Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, Minghui Qiu	This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session.
26	Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning	Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu	In this work, we investigate the potential of leveraging knowledge graph (KG) in dealing with these issues of RL methods for IRS, which provides rich side information for recommendation decision making.
27	Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation	Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma	In this paper, we propose a novel Jointly Non-Sampling learning model for Knowledge graph enhanced Recommendation (JNSKR).
28	AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction	Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, Zhenguo Li	In this work, we use AutoML to seek useful high-order feature interactions to train on without manual feature selection.
29	KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation	Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, Shaozhang Niu, Jimmy Huang	Inspired by the availability of knowledge graph (KG), we propose a novel Knowledge-guidEd Reinforcement Learning model (KERL for short) for fusing KG information into a RL framework for sequential recommendation.
30	CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems	Ze Wang, Guangyan Lin, Huobin Tan, Qinghong Chen, Xiyang Liu	In this paper, we propose a novel method named Collaborative Knowledge-aware Attentive Network (CKAN) which explicitly encodes the collaborative signals by collaboration propagation and proposes a natural way of combining collaborative signals with knowledge associations together.
31	CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network	Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun	Inspired by the advances made in review-based recommendation, we propose to model user preference transfer at aspect-level derived from reviews.
32	Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs	Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie	In this paper, we address these issues by better supervising the path finding process.
33	Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation	Jianming Zheng, Fei Cai, Honghui Chen	In this paper, we propose a unified fine-tuning architecture, incorporated with scenario knowledge for event representation, i.e., UniFA-S, which mainly consists of a unified fine-tuning architecture (UniFA) and a scenario-level variational auto-encoder (S-VAE).
34	Ranking-Incentivized Quality Preserving Content Modification	Gregory Goren, Oren Kurland, Moshe Tennenholtz, Fiana Raiber	We present an automatic method for quality-preserving modification of document content — i.e., maintaining content quality — so that the document is ranked higher for a query by a non-disclosed ranking function whose rankings can be observed.
35	On Understanding Data Worker Interaction Behaviors	Lei Han, Tianwa Chen, Gianluca Demartini, Marta Indulska, Shazia Sadiq	In this work, we aim at understanding the behaviors of data workers in discovering data quality issues, and how these behavioral observations relate to their performance.
36	Creating a Children-Friendly Reading Environment via Joint Learning of Content and Human Attention	Guoxiu He, Yangyang Kang, Zhuoren Jiang, Jiawei Liu, Changlong Sun, Xiaozhong Liu, Wei Lu	In this study, we propose a novel framework, Joint Learning of Content and Human Attention (GoodMan), to identify indecent readings by augmenting natural language understanding models with large scale human reading behaviors (dwell time per page) on portable devices.
37	Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates	Zheng Liu, Jianxun Lian, Junhan Yang, Defu Lian, Xing Xie	In this work, a novel personalized candidate generation paradigm, Octopus, is proposed, which is remarkable for its comprehensiveness and elasticity.
38	The Cortical Activity of Graded Relevance	Zuzana Pinkosova, William J. McGeown, Yashar Moshfeghi	In this paper, we aim to investigate the brain activity associated with relevance when it is treated as a graded concept.
39	Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback	Yuta Saito	To overcome these limitations, we propose a model-agnostic meta-learning method inspired by the asymmetric tri-training framework for unsupervised domain adaptation.
40	Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation	Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng Zhang, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma	In this work, a novel user preference representation called Preference Hash (PreHash) is proposed to model large scale users, including rare-interaction ones.
41	Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations	Krisztian Balog, Filip Radlinski	Specifically, this paper presents a first proposal of how to measure the quality of explanations along seven common goal dimensions catalogued in the literature.
42	Bayesian Inferential Risk Evaluation On Multiple IR Systems	Rodger Benham, Ben Carterette, J. Shane Culpepper, Alistair Moffat	In this work, we propose a Bayesian approach where multiple challengers are compared to a single champion.
43	How to Measure the Reproducibility of System-oriented IR Experiments	Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff	To address these issues, we compare several measures to objectively quantify to what extent we have replicated or reproduced a system-oriented IR experiment.
44	Good Evaluation Measures based on Document Preferences	Tetsuya Sakai, Zhaohao Zeng	The present study addresses exactly this question, after formally defining two classes of preference-based measures called Pref measures and Δ-measures.
45	Preference-based Evaluation Metrics for Web Image Search	Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Haitian Chen, Min Zhang, Shaoping Ma	In this paper, we provide a thorough comparison of variants of preference judgments for web image search.
46	Models Versus Satisfaction: Towards a Better Understanding of Evaluation Metrics	Fan Zhang, Jiaxin Mao, Yiqun Liu, Xiaohui Xie, Weizhi Ma, Min Zhang, Shaoping Ma	Specifically, we want to investigate whether the metrics that are well calibrated with user behavior data can perform as well in estimating user satisfaction.
47	Cascade or Recency: Constructing Better Evaluation Metrics for Session Search	Fan Zhang, Jiaxin Mao, Yiqun Liu, Weizhi Ma, Min Zhang, Shaoping Ma	To take both the cascade hypothesis and the recency effect into the design of session search evaluation metrics, we propose Recency-aware Session-based Metrics (RSMs) to simultaneously characterize users’ examination process with a browsing model and cognitive process with a utility accumulation model.
48	Operationalizing the Legal Principle of Data Minimization for Personalization	Asia J. Biega, Peter Potash, Hal Daumé, Fernando Diaz, Michèle Finck	In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization.
49	Learning Personalized Risk Preferences for Recommendation	Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang	In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users’ purchasing behaviors.
50	Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines	Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, Zibin Zheng	In our work, we propose the first method for the certifiable robustness of factorization machines with respect to the discrete perturbation on input features.
51	Controlling Fairness and Bias in Dynamic Learning-to-Rank	Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims	We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist).
52	Can The Crowd Identify Misinformation Objectively?: The Effects of Judgment Scale and Assessor’s Background	Kevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina, Stefano Mizzaro, Gianluca Demartini	In this paper, we follow a different approach and rely on (non-expert) crowd workers.
53	Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems	Ziwei Zhu, Jianling Wang, James Caverlee	Concretely, we formalize the concepts of ranking-based statistical parity and equal opportunity as two measures of item under-recommendation bias. Then, we empirically show that one of the most widely adopted algorithms — Bayesian Personalized Ranking — produces biased recommendations, which motivates our effort to propose the novel debiased personalized ranking model.
54	What Makes a Top-Performing Precision Medicine Search Engine?: Tracing Main System Features in a Systematic Way	Erik Faessler, Michel Oleynik, Udo Hahn	In order to overcome this explanatory gap, we first determined optimal feature configurations using the Sequential Model-based Algorithm Configuration (SMAC) program and applied its output to a BM25-based search engine.
55	Accelerated Convergence for Counterfactual Learning to Rank	Rolf Jagerman, Maarten de Rijke	In this paper we show that the convergence rate of SGD approaches with IPS-weighted gradients suffers from the large variance introduced by the IPS weights: convergence is slow, especially when there are large IPS weights.
56	DVGAN: A Minimax Game for Search Result Diversification Combining Explicit and Implicit Features	Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-Rong Wen	To tackle this problem, we propose a supervised diversification framework based on Generative Adversarial Network (GAN).
57	Policy-Aware Unbiased Learning to Rank for Top-k Rankings	Harrie Oosterhuis, Maarten de Rijke	Together, our contributions introduce the first policy-aware unbiased LTR approach that learns from top-k feedback and optimizes top-k metrics.
58	SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval	Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, Jirong Wen	In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size.
59	Reinforcement Learning to Rank with Pairwise Policy Gradient	Jun Xu, Zeng Wei, Long Xia, Yanyan Lan, Dawei Yin, Xueqi Cheng, Ji-Rong Wen	To deal with the issues, we propose a novel policy gradient algorithm in which the gradients are determined using pairwise comparisons of two document lists sampled within the same query.
60	Humor Detection in Product Question Answering Systems	Yftah Ziser, Elad Kravi, David Carmel	In this study we present a deep-learning framework for detecting humorous questions in PQA systems.
61	Training Curricula for Open Domain Answer Re-Ranking	Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder	In this work, we apply this idea to the training of neural answer rankers using curriculum learning.
62	Open-Retrieval Conversational Question Answering	Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, Mohit Iyyer	To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems.
63	Learning to Ask Screening Questions for Job Postings	Baoxu Shi, Shan Li, Jaewon Yang, Mustafa Emre Kazdagli, Qi He	To add screening questions to all 20M active jobs at Linked In, we propose a new task that aims to automatically generate screening questions for a given job posting.
64	Match²: A Matching over Matching Model for Similar Question Identification	Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xueqi Cheng, Hui Jiang, Xiaozhao Wang	In this work, we propose atwo-side usage, which leverages the answer as a bridge of the two questions.
65	Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling	Wenxuan Zhang, Yang Deng, Wai Lam	In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating the ranking.
66	ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance	Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, Hongbo Deng	To this end, we propose an entire space adaptation model (ESAM) to address this problem from the perspective of domain adaptation (DA).
67	Table Search Using a Deep Contextualized Language Model	Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Yinan Xu, Brian D. Davison	In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval.
68	Convolutional Embedding for Edit Distance	Xinyan DAI, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng	In this paper, we propose a deep learning pipeline (called CNN-ED) that embeds edit distance into Euclidean distance for fast approximate similarity search.
69	ASiNE: Adversarial Signed Network Embedding	Yeon-Chang Lee, Nayoun Seo, Kyungsik Han, Sang-Wook Kim	Motivated by a success of generative adversarial networks (GAN) in various domains including information retrieval, we propose a novel signed network embedding framework, ASiNE, which represents each node of a given signed network as a low-dimensional vector based on the adversarial learning.
70	Efficient Graph Query Processing over Geo-Distributed Datacenters	Ye Yuan, Delong Ma, Zhenyu Wen, Yuliang Ma, Guoren Wang, Lei Chen	In this paper, we propose GeoGraph –a universal framework to support efficient geo-distributed graph query processing based on clustering datacenters and meta-graph, while reducing the inter-datacenter communication.
71	Spatio-Temporal Dual Graph Attention Network for Query-POI Matching	Zixuan Yuan, Hao Liu, Yanchi Liu, Denghui Zhang, Fei Yi, Nengjun Zhu, Hui Xiong	To this end, in this paper, we develop a spatio-temporal dual graph attention network ~(STDGAT), which can jointly model dynamic situational context and users’ sequential behaviors for intelligent query-POI matching.
72	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong Zhang, Meng Wang	In this work, we aim to simplify the design of GCN to make it more concise and appropriate for recommendation.
73	GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation	Zhixiang He, Chi-Yin Chow, Jia-Dong Zhang	To this end, we propose a model, named GAME to learn the Graphical and Attentive Multi-view Embeddings (i.e., representations) for the groups, users and items from the independent view and counterpart views based on the interaction graph.
74	Multi-behavior Recommendation with Graph Convolutional Networks	Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, Yong Li	In this work, we approach this problem by innovatively constructing a unified graph to represent multi-behavior data and proposing a new model named MBGCN (short for Multi-Behavior Graph Convolutional Network ).
75	GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation	Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen	In this paper, we propose a G lobal A ttributed G raph (GAG) neural network model with a Wasserstein reservoir for the SSR problem.
76	Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach	Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, Meng Wang	To this end, in this paper, we define these two tasks in an attributed user-item bipartite graph, and propose an Adaptive Graph Convolutional Network(AGCN) approach for joint item recommendation and attribute inference.
77	GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection	Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, Lizhen Cui	In this paper, we propose GraphRfi – a GCN-based user representation learning framework to perform robust recommendation and fraudster detection in a unified way.
78	Using Phoneme Representations to Build Predictive Models Robust to ASR Errors	Anjie Fang, Simone Filice, Nut Limsopatham, Oleg Rokhlenko	To make NLU models more robust to such errors, we propose novel phonetic-aware text representations.
79	Knowledge Enhanced Personalized Search	Shuqi Lu, Zhicheng Dou, Chenyan Xiong, Xiaojie Wang, Ji-Rong Wen	This paper presents a knowledge graph enhanced personalized search model, KEPS.
80	Streaming Graph Neural Networks	Yao Ma, Ziyi Guo, Zhaocun Ren, Jiliang Tang, Dawei Yin	Hence, in this paper, we propose DyGNN, a Dynamic Graph Neural Network model, which can model the dynamic information as the graph evolving.
81	An Eye Tracking Study of Web Search by People With and Without Dyslexia	Srishti Palani, Adam Fourney, Shane Williams, Kevin Larson, Irina Spiridonova, Meredith Ringel Morris	In this paper, we collected and analyzed eye-tracking, search log, and self-report data from 27 participants (14 with dyslexia) to confirm that searchers with dyslexia struggle with all stages of the search process and have markedly different gaze patterns and search behavior that reflect the strategies used and challenges faced.
82	DGL-KE: Training Knowledge Graph Embeddings at Scale	Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis	This paper presents DGL-KE, an open-source package to efficiently compute knowledge graph embeddings.
83	Neural Interactive Collaborative Filtering	Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, Dawei Yin	In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback.
84	Fashion Compatibility Modeling through a Multi-modal Try-on-guided Scheme	Xue Dong, Jianlong Wu, Xuemeng Song, Hongjun Dai, Liqiang Nie	In light of this, we propose a multi-modal try-on-guided compatibility modeling scheme to jointly characterize the discrete interaction and try-on appearance of the outfit.
85	Spatial Object Recommendation with Hints: When Spatial Granularity Matters	Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao Liu, Hui Xiong	In this paper, we study how to support top-k spatial object recommendations at varying levels of spatial granularity, enabling spatial objects at varying granularity, such as a city, suburb, or building, as a Point of Interest (POI).
86	Product Bundle Identification using Semi-Supervised Learning	Hen Tzaban, Ido Guy, Asnat Greenstein-Messica, Arnon Dagan, Lior Rokach, Bracha Shapira	In this work, we present a comprehensive study of bundle identification on a large e-commerce website.
87	Coding Electronic Health Records with Adversarial Reinforcement Path Generation	Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jian-Yun Nie, Jun Ma, Maarten de Rijke	We propose a coarse-to-fine ICD path generation framework, named Reinforcement Path Generation Network (RPGNet), that implements EHR coding with a Path Generator (PG) and a Path Discriminator (PD).
88	Degree-Aware Alignment for Entities in Tail	Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan	For pre-alignment, we propose to amplify long-tail entities, which are of relatively weak structural information, with entity name information that is generally available (but overlooked) in the form of concatenated power mean word embeddings.
89	Regional Relation Modeling for Visual Place Recognition	Yingying Zhu, Biao Li, Jiong Wang, Zhou Zhao	In this paper, we propose a regional relation module which models the regional relationships and converts the convolutional feature maps to the relational feature maps.
90	A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data	Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, Zhong Ming	In this paper, we focus on solving the bias problems in a recommender system via a uniform data.
91	Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation	Elisa Mena-Maldonado, Rocío Cañamares, Pablo Castells, Yongli Ren, Mark Sanderson	In this paper we research the extent to which false-positive metrics agree or disagree with true-positive metrics in the offline evaluation of recommender systems.
92	Leveraging Social Media for Medical Text Simplification	Nikhil Pattisapu, Nishant Prabhu, Smriti Bhati, Vasudeva Varma	To overcome these challenges, we propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text.
93	Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning	Wenhui Yu, Zheng Qin	To address this gap, we design an adaptive sampler based on noisy-label robust learning for implicit feedback data.
94	MaHRL: Multi-goals Abstraction Based Deep Hierarchical Reinforcement Learning for Recommendations	Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng Yan	To solve the inherent problem in hierarchical reinforcement learning, we propose a novel multi-goals abstraction based deep hierarchical reinforcement learning algorithm (MaHRL).
95	Towards Question-based Recommender Systems	Jie Zou, Yifan Chen, Evangelos Kanoulas	In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions.
96	Try This Instead: Personalized and Interpretable Substitute Recommendation	Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang	In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives.
97	Towards Linking Camouflaged Descriptions to Implicit Products in E-commerce	Longtao Huang, Bo Yuan, Rong Zhang, Quan Lu	In this paper, we introduce three types of context that could help to infer implicit entity from camouflaged descriptions and propose an end-to-end contextual representation model to capture the effect of different context.
98	Distributed Equivalent Substitution Training for Large-Scale Recommender Systems	Haidong Rong, Yangzihao Wang, Feihu Zhou, Junjie Zhai, Haiyang Wu, Rui Lan, Fan Li, Han Zhang, Yuekui Yang, Zhenyu Guo, Di Wang	We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
99	Query Resolution for Conversational Search with Limited Supervision	Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, Maarten de Rijke	In this work we focus on multi-turn passage retrieval as a crucial component of conversational search.
100	Self-Supervised Reinforcement Learning for Recommender Systems	Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose	In this paper, we propose self-supervised reinforcement learning for sequential recommendation tasks.
101	Generative Attribute Manipulation Scheme for Flexible Fashion Search	Xin Yang, Xuemeng Song, Xianjing Han, Haokun Wen, Jie Nie, Liqiang Nie	In this work, we aim to investigate the practical task of flexible fashion search with attribute manipulation, where users can retrieve the target fashion items by replacing the unwanted attributes of an available query image with the desired ones (e.g., changing the collar attribute from v-neck to round).
102	How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models	Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Felice Antonio Merra	Toward this goal, this work presents a systematic and in-depth study by using an analytical modeling approach built on a regression model to test the hypothesis of whether URM properties can impact the outcome of CF recommenders under a shilling attack.
103	DPLCF: Differentially Private Local Collaborative Filtering	Chen Gao, Chao Huang, Dongsheng Lin, Depeng Jin, Yong Li	In this work, we propose a general framework named differentially private local collaborative filtering for recommendation.
104	Content-aware Neural Hashing for Cold-start Recommendation	Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma	We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which generates binary hash codes for users and items, such that the highly efficient Hamming distance can be used for estimating user-item relevance.
105	Meta Matrix Factorization for Federated Rating Predictions	Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, Xiuzhen Cheng	To this end, we introduce a novel federated matrix factorization (MF) framework, named meta matrix factorization (MetaMF), that is able to generate private item embeddings and RP models with a meta network.
106	The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation	Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims	In this paper, we investigate how we can support a greater degree of user control in such systems by changing the way the system allows people to gauge the consequences of their feedback actions.
107	Disentangled Graph Collaborative Filtering	Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua	In this work, we pay special attention to user-item relationships at the finer granularity of user intents.
108	Domain-Adaptive Neural Automated Essay Scoring	Yue Cao, Hanqi Jin, Xiaojun Wan, Zhiwei Yu	In this paper, we propose a domain-adaptive framework to improve the domain adaptability of AES models.
109	ADORE: Aspect Dependent Online REview Labeling for Review Generation	Parisa Kaghazgaran, Jianling Wang, Ruihong Huang, James Caverlee	Hence, toward enabling a writing assistant framework to help users post online reviews, this paper proposes a scalable labeling method for bootstrapping aspect and sentiment labels.Concretely, the proposed approach ?
110	Finding the Best of Both Worlds: Faster and More Robust Top-k Document Retrieval	Omar Khattab, Mohammad Hammoud, Tamer Elsayed	In this paper, we conduct the first extensive comparison between ten effective strategies, many of which were never compared before to our knowledge, examining their efficiency under five representative ranking models.
111	Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste	Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione, Ben Carterette	We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem.
112	Learning with Weak Supervision for Email Intent Detection	Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, Susan Dumais	In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.
113	3D Self-Attention for Unsupervised Video Quantization	Jingkuan Song, Ruimin Lang, Xiaosu Zhu, Xing Xu, Lianli Gao, Heng Tao Shen	In this paper, we make a first attempt to combine quantization method with video retrieval called 3D-UVQ, which obtains high retrieval accuracy with low storage cost.
114	Modeling Personalized Item Frequency Information for Next-basket Recommendation	Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang	Given this inherent limitation of RNNs, we propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these critical signals.
115	Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation	Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang, Hari Sundaram	This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning.
116	Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation	Wenjing Meng, Deqing Yang, Yanghua Xiao	These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation.
117	Next-item Recommendation with Sequential Hypergraphs	Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee	Thus, we are motivated to develop a novel next-item recommendation framework empowered by sequential hypergraphs.
118	Encoding History with Context-aware Representation Learning for Personalized Search	Yujia Zhou, Zhicheng Dou, Ji-Rong Wen	In this paper, we propose to encode history with context-aware representation learning to enhance the representation of current query, which is a direct way to clarify the user’s information need.
119	Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation	Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, James Caverlee	Hence, this paper proposes a novel model designed to overcome these drawbacks while delivering strong cold start performance.
120	Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search	Helia Hashemi, Hamed Zamani, W. Bruce Croft	In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources that weights each term in the conversation.
121	Investigating Reference Dependence Effects on User Search Interaction and Satisfaction: A Behavioral Economics Perspective	Jiqun Liu, Fangyuan Han	To address this gap, our work seeks to 1) understand the effects of reference points on search behavior and satisfaction at both query and session levels; 2) apply the knowledge of reference dependence in predicting users’ search decisions and variations in level of satisfaction.
122	DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation	Chuan Meng, Pengjie Ren, Zhumin Chen, Weiwei Sun, Zhaochun Ren, Zhaopeng Tu, Maarten de Rijke	In this paper, we target the Knowledge Selection (KS) task, a key ingredient in KGC, that is aimed at selecting the appropriate knowledge to be used in the next response.
123	What If Bots Feel Moods?	Lisong Qiu, Yingwai Shiu, Pingping Lin, Ruihua Song, Yue Liu, Dongyan Zhao, Rui Yan	In this paper, to implement a more empathetic retrieval-based conversation system, we incorporate emotional factors into context-response matching from two aspects: 1) On top of semantic matching, we propose an emotion-aware transition network to model the dynamic emotional flow and enhance context-response matching in retrieval-based dialogue systems with learnt intrinsic emotion features through a multi-task learning framework; 2) We design several flexible controlling mechanisms to customize social bots in terms of emotion.
124	Expressions of Style in Information Seeking Conversation with an Agent	Paul Thomas, Daniel McDuff, Mary Czerwinski, Nick Craswell	We see that “style” can be measured in human-to-agent conversation, although it looks somewhat different to style in human-to-human conversation and does not correlate with self-reported preferences.
125	Analyzing and Learning from User Interactions for Search Clarification	Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell, Susan T. Dumais	In this paper, we conduct a comprehensive study by analyzing large-scale user interactions with clarifying questions in a major web search engine.
126	A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss	Hou Pong Chan, Wang Chen, Irwin King	To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks.
127	Enhancing Text Classification via Discovering Additional Semantic Clues from Logograms	Chen Qian, Fuli Feng, Lijie Wen, Li Lin, Tat-Seng Chua	In this paper, by using a phonographic labeled corpus and its machine-translated logographic corpus both, we devise a framework to explore the central theme of utilizing logograms as a "semantic detection assistant”.
128	Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation	Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang	In this paper, we study the problem of personalized video highlight recommendation with rich visual content.
129	Web-to-Voice Transfer for Product Recommendation on Voice	Rongting Zhang, Jie Yang	This paper presents TransV, a novel Web-to-Voice neural transfer network that allows for effective transfer of customers’ shopping patterns from the Web to Voice, while taking into account customers’ distinct purchase patterns on Voice.
130	Minimally Supervised Categorization of Text with Metadata	Yu Zhang, Yu Meng, Jiaxin Huang, Frank F. Xu, Xuan Wang, Jiawei Han	In recognition of these two challenges, we propose MetaCat, a minimally supervised framework to categorize text with metadata.
131	Joint Aspect-Sentiment Analysis with Minimal User Guidance	Honglei Zhuang, Fang Guo, Chao Zhang, Liyuan Liu, Jiawei Han	We aim to build an aspect-based sentiment analysis model from an unlabeled corpus with minimal guidance from users, i.e., only a small set of seed words for each aspect class and each sentiment class.
132	AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for Addressing Cold-Start Problems	Dong-Kyu Chae, Jihoo Kim, Duen Horng Chau, Sang-Wook Kim	Different from (and complementary to) data imputation, this paper presents AR-CF, which stands for Augmented Reality CF, a novel framework for addressing the cold-start problems by generating virtual, but plausible neighbors for cold-start users or items and augmenting them to the rating matrix as additional information for CF models.
133	Studying Product Competition Using Representation Learning	Fanglin Chen, Xiao Liu, Davide Proserpio, Isamar Troncoso, Feiyu Xiong	We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec, to study product-level competition, when the number of products is large.
134	Deep Critiquing for VAE-based Recommender Systems	Kai Luo, Hojin Yang, Ga Wu, Scott Sanner	In this paper, we propose a Variational Autoencoder (VAE) based critiquing system to mitigate these issues and improve overall performance.
135	GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation	Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei Zhang, Hao Yang, Hari Sundaram	To overcome group interaction sparsity, we propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users’ individual preferences to each group.
136	Neighbor Interaction Aware Graph Convolution Networks for Recommendation	Jianing Sun, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Xiuqiang He, Chen Ma, Mark Coates	To resolve the above limitations, in this paper, we propose a novel framework NIA-GCN, which can explicitly model the relational information between neighbor nodes and exploit the heterogeneous nature of the user-item bipartite graph.
137	A Generic Network Compression Framework for Sequential Recommender Systems	Yang Sun, Fajie Yuan, Min Yang, Guoao Wei, Zhou Zhao, Duo Liu	To resolve the issues, we propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed.
138	Learning Efficient Representations of Mouse Movements to Predict User Attention	Ioannis Arapakis, Luis A. Leiva	We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks that can predict user attention to direct displays, such as SERP advertisements.
139	Query Reformulation in E-Commerce Search	Sharon Hirsch, Ido Guy, Alexander Nus, Arnon Dagan, Oren Kurland	We present the first large-scale and in-depth study of query reformulations performed by users of e-commerce search; the study is based on the query logs of eBay’s search engine.
140	Generating Images Instead of Retrieving Them: Relevance Feedback on Generative Adversarial Networks	Antti Ukkonen, Pyry Joona, Tuukka Ruotsalo	Here, we present a methodology for generating images matching the user intention instead of retrieving them.
141	Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval	Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua	To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos.
142	Nonlinear Robust Discrete Hashing for Cross-Modal Retrieval	Zhan Yang, Jun Long, Lei Zhu, Wenti Huang	To solve this limitation, we propose a novel method termed Nonlinear Robust Discrete Hashing (NRDH), for cross-modal retrieval.
143	Employing Personal Word Embeddings for Personalized Search	Jing Yao, Zhicheng Dou, Ji-Rong Wen	In this paper, we propose to solve the problem of personalized search in an alternative way.
144	Query Rewriting for Voice Shopping Null Queries	Iftah Gamzu, Marina Haikin, Nissim Halabi	We present a new approach for pre-retrieval QR of voice shopping null queries.
145	Joint-modal Distribution-based Similarity Hashing for Large-scale Unsupervised Deep Cross-modal Retrieval	Song Liu, Shengsheng Qian, Yang Guan, Jiawei Zhan, Long Ying	To overcome these limitations, we propose a novel unsupervised deep cross-modal hashing method called Joint-modal Distribution-based Similarity Hashing (JDSH) for large-scale cross-modal retrieval.
146	Learning Colour Representations of Search Queries	Paridhi Maheshwari, Manoj Ghuhan, Vishwa Vinay	In this work, we consider the role of colour in this relevance matching process.
147	Web Table Retrieval using Multimodal Deep Learning	Roee Shraga, Haggai Roitman, Guy Feigenblat, Mustafa Cannim	We address the web table retrieval task, aiming to retrieve and rank web tables as whole answers to a given information need.
148	Online Collective Matrix Factorization Hashing for Large-Scale Cross-Media Retrieval	Di Wang, Quan Wang, Yaqiang An, Xinbo Gao, Yumin Tian	In this paper, we propose Online Collective Matrix Factorization Hashing (OCMFH) based on collective matrix factorization hashing (CMFH), which can adaptively update hash codes of old data according to dynamic changes of hash model without accessing to old data.
149	Correlated Features Synthesis and Alignment for Zero-shot Cross-modal Retrieval	Xing Xu, Kaiyi Lin, Huimin Lu, Lianli Gao, Heng Tao Shen	To address these issues, we propose a novel Correlated Feature Synthesis and Alignment (CFSA) approach to integrate multimodal feature synthesis, common space learning and knowledge transfer for ZS-CMR.
150	HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation	Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, Fan Li	Specifically, to solve the next-POI recommendation task, we propose a novel hyperbolic metric embedding (HME) model, which projects the check-in data into a hyperbolic space.
151	Dual Sequential Network for Temporal Sets Prediction	Leilei Sun, Yansong Bai, Bowen Du, Chuanren Liu, Hui Xiong, Weifeng Lv	To address these issues, this paper provides a novel sets prediction method, called DSNTSP (Dual Sequential Network for Temporal Sets Prediction).
152	Group-Aware Long- and Short-Term Graph Representation Learning for Sequential Group Recommendation	Wen Wang, Wei Zhang, Jun Rao, Zhijie Qiu, Bo Zhang, Leyu Lin, Hongyuan Zha	To address this, we devise a Group-aware Long- and Short-term Graph Representation Learning approach, namely GLS-GRL, for sequential group recommendation.
153	Time Matters: Sequential Recommendation with Complex Temporal Information	Wenwen Ye, Shuaiqiang Wang, Xu Chen, Xuepeng Wang, Zheng Qin, Dawei Yin	This paper aims to systematically investigate the effects of the temporal information in sequential recommendations.
154	Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation	Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang	In this paper, we delve on the task of effectively learning a single user representation that can be applied to a diversity of tasks, from cross-domain recommendations to user profile predictions.
155	How to Retrain Recommender System?: A Sequential Meta-Learning Method	Yang Zhang, Fuli Feng, Chenxu Wang, Xiangnan He, Meng Wang, Yan Li, Yongdong Zhang	In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community.
156	Dynamic Clustering with Discrete Time Event Prediction	Karan Aggarwal, Georgios Theocharous, Anup B. Rao	We propose methods to perform a time-varying clustering along with event predictions in a unified domain agnostic framework.
157	Leveraging Transitions of Emotions for Sarcasm Detection	Ameeta Agrawal, Aijun An, Manos Papagelis	In order to explore the role of transitions in affective states, we formulate the task of sarcasm detection as a sequence classification problem by leveraging the natural shifts in various emotions over the course of a piece of text.
158	JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search	Ali Ahmadvand, Surya Kallumadi, Faizan Javed, Eugene Agichtein	In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query’s commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.
159	Choppy: Cut Transformer for Ranked List Truncation	Dara Bahri, Yi Tay, Che Zheng, Donald Metzler, Andrew Tomkins	In this work, we propose Choppy, an assumption-free model based on the widely successful Transformer architecture, to the ranked list truncation problem.
160	Studying Ransomware Attacks Using Web Search Logs	Chetan Bansal, Pantazis Deligiannis, Chandra Maddila, Nikitha Rao	We do the first study on mining insights about ransomware attacks by analyzing query logs from Bing web search engine.
161	A Transformer-based Embedding Model for Personalized Product Search	Keping Bi, Qingyao Ai, W. Bruce Croft	Aware of these limitations, we propose a transformer-based embedding model (TEM) for personalized product search, which could dynamically control the influence of personalization by encoding the sequence of query and user’s purchase history with a transformer architecture.
162	Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval	Limin Chen, Zhiwen Tang, Grace Hui Yang	Our paper addresses this issue by using domain randomization to synthesize more relevant documents for the training.
163	Metadata Matters in User Engagement Prediction	Xiang Chen, Saayan Mitra, Viswanathan Swaminathan	In this paper, we propose to include the metadata feature, which captures the visual appearance of the ad, in the user engagement prediction task.
164	Context-Aware Term Weighting For First Stage Passage Retrieval	Zhuyun Dai, Jamie Callan	This paper proposes a Deep Contextualized Term Weighting framework (DeepCT) that maps the contextualized term representations from BERT to into context-aware term weights for passage retrieval.
165	Summarizing and Exploring Tabular Data in Conversational Search	Shuo Zhang, Zhuyun Dai, Krisztian Balog, Jamie Callan	We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset.
166	Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification	Xin Dong, Yaxin Zhu, Yupeng Zhang, Zuohui Fu, Dongkuan Xu, Sen Yang, Gerard de Melo	To address this, we present a semi- supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations.
167	Response Quality in Human-Chatbot Collaborative Systems	Jiepu Jiang, Naman Ahuja	We report the results of a crowdsourcing user study for evaluating the effectiveness of human-chatbot collaborative conversation systems, which aim to extend the ability of a human user to answer another person’s requests in a conversation using a chatbot.
168	High-Precision Extraction of Emerging Concepts from Scientific Literature	Daniel King, Doug Downey, Daniel S. Weld	We present an unsupervised concept extraction method for scientific literature that achieves much higher precision than previous work. To stimulate research in this area, we release our code and data.
169	Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic	Dingcheng Li, Xu Li, Jun Wang, Ping Li	In this paper, we propose a reinforcement learning based large scale multi-objective ranking system for optimizing short-video recommendation on an industrial video sharing platform.
170	Auto-annotation for Voice-enabled Entertainment Systems	Wenyan Li, Ferhan Ture	We present an auto-annotation system, which provides high quality training data without any hand-labeled audios by detecting speech recognition errors and providing possible fixes.
171	Domain Adaptation with Reconstruction for Disaster Tweet Classification	Xukun Li, Doina Caragea	We propose to investigate the effectiveness of the Domain Reconstruction Classification Network (DRCN) approach on disaster tweets.
172	Recipe Retrieval with Visual Query of Ingredients	Yen-Chieh Lien, Hamed Zamani, W. Bruce Croft	In this paper, we revisit the task of recipe retrieval by taking images of ingredients as input queries, and retrieving cuisine images by incorporating visual information of ingredients through a deep convolutional neural network.
173	Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection	Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, Hao Peng	In this paper, we introduce these inconsistencies and design a new GNN framework, GraphConsis, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features; (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability; (3) for the relation inconsistency, we learn the relation attention weights associated with the sampled nodes.
174	Expansion via Prediction of Importance with Contextualization	Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder	We address this problem with a representation-based ranking approach that: (1) explicitly models the importance of each term using a contextualized language model; (2) performs passage expansion by propagating the importance to similar terms; and (3) grounds the representations in the lexicon, making them interpretable.
175	Reranking for Efficient Transformer-based Answer Selection	Yoshitomo Matsubara, Thuy Vu, Alessandro Moschitti	In this paper, we show that standard and efficient neural rerankers can be used to reduce the amount of sentence candidates fed to Transformer models without hurting Accuracy, thus improving efficiency up to four times.
176	Combining Contextualized and Non-contextualized Query Translations to Improve CLIR	Suraj Nair, Petra Galuscakova, Douglas W. Oard	This paper presents evidence that combining such context-dependent translation probabilities with context-independent translation probabilities learned from the same parallel corpus can yield improvements in the effectiveness of cross-language ranked retrieval.
177	Proactive Suggestion Generation: Data and Methods for Stepwise Task Assistance	Elnaz Nouri, Robert Sim, Adam Fourney, Ryen W. White	We propose two types of stepwise suggestions: multiple-choice response generation and text generation.
178	Contextual Re-Ranking with Behavior Aware Transformers	Chen Qu, Chenyan Xiong, Yizhe Zhang, Corby Rosset, W. Bruce Croft, Paul Bennett	In this work, we focus on the contextual document ranking task, which deals with the challenge of user interaction modeling for conversational search.
179	Towards Explainable Retrieval Models for Precision Medicine Literature Search	Jiaming Qu, Jaime Arguello, Yue Wang	Using structured relevance judgment data from the TREC Precision Medicine track, we propose novel retrieval models that emulate how medical experts make structured relevance judgments.
180	Search Result Explanations Improve Efficiency and Trust	Jerome Ramos, Carsten Eickhoff	This paper presents an investigation of system quality when different degrees of explanation are provided on search engine result pages.
181	Query by Example for Cross-Lingual Event Retrieval	Sheikh Muhammad Sarwar, James Allan	We propose a Query by Example (QBE) setting for cross-lingual event retrieval. To evaluate our approach we construct an event retrieval dataset from ACE which is an existing event detection dataset.
182	A Test Collection for Relevance and Sensitivity	Mahmoud F. Sayed, William Cox, Jonah Lynn Rivera, Caitlin Christian-Lamb, Modassir Iqbal, Douglas W. Oard, Katie Shilton	This paper describes the development of such a test collection that is based on the Avocado Research Email Collection.
183	A Study of Methods for the Generation of Domain-Aware Word Embeddings	Dominic Seyler, ChengXiang Zhai	In this paper, we study three methods for creating domain-aware word embeddings based on both general and domain-specific text corpora, including concatenation of embedding vectors, weighted fusion of text data, and interpolation of aligned embedding vectors.
184	Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants	Christophe Van Gysel, Manos Tsagkias, Ernest Pusateri, Ilya Oparin	We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subsequently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA.
185	Data Poisoning Attacks against Differentially Private Recommender Systems	Soumya Wadhwa, Saurabh Agrawal, Harsh Chaudhari, Deepthi Sharma, Kannan Achan	In this paper, we study the effectiveness of differential privacy against such attacks on matrix factorization based collaborative filtering systems.
186	Immersive Search: Using Virtual Reality to Examine How a Third Dimension Impacts the Searching Process	Austin R. Ward, Rob Capra	In this paper, we present results from an exploratory study to investigate users’ behaviors and preferences for three different styles of search results presentation in a virtual reality (VR) head-mounted display (HMD).
187	Having Your Cake and Eating it Too: Training Neural Retrieval for Language Inference without Losing Lexical Match	Vikas Yadav, Steven Bethard, Mihai Surdeanu	To mitigate this limitation, we introduce a supervised RoBERTa QA method that is trained to mimic the behavior of BM25 and the soft-matching idea behind embedding-based alignment methods.
188	Neural Concept Map Generation for Effective Document Classification with Interpretable Structured Summarization	Carl Yang, Jieyu Zhang, Haonan Wang, Bangzheng Li, Jiawei Han	In this work, we bridge the gap between concept map construction and neural network models, by designing doc2graph, a novel weakly-supervised text-to-graph neural network, which generates concept maps in the middle and is trained towards document-level tasks like document classification.
189	A Case Study of Multi-class Classification with Diversified Precision Recall Requirements for Query Disambiguation	Yingrui Yang, Christopher Miller, Peng Jiang, Azadeh Moghtaderi	We introduce a new metric for measuring the performance of multi-class classifiers.
190	A Study of Neural Matching Models for Cross-lingual IR	Puxuan Yu, James Allan	In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs).
191	Social Media User Geolocation via Hybrid Attention	Cheng Zheng, Jyun-Yu Jiang, Yichao Zhou, Sean D. Young, Wei Wang	To address this challenge, we propose a novel end-to-end framework, Hybrid-attentive User Geolocation (HUG), to jointly model post texts and user interactions in social media.
192	Personalized Query Suggestions	Jianling Zhong, Weiwei Guo, Huiji Gao, Bo Long	Here, we present a sequence-to-sequence-model–based query suggestion framework that is capable of modeling structured, personalized features and unstructured query texts naturally.
193	Feature Transformation for Neural Ranking Models	Honglei Zhuang, Xuanhui Wang, Michael Bendersky, Marc Najork	In this paper, we aim to answer this question by providing empirical evidence for learning-to-rank tasks.
194	MetaGen: An academic Meta-review Generation system	Chaitanya Bhatia, Tribikram Pradhan, Sukomal Pal	In this paper, we present MetaGen, a novel meta-review generation system which takes the peer reviews as input and produces an assistive meta-review.
195	Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity	Paheli Bhattacharya, Kripabandhu Ghosh, Arindam Pal, Saptarshi Ghosh	We propose to augment the PCNet with the hierarchy of legal statutes, to form a heterogeneous network Hier-SPCNet.
196	DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain	Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, Jing Xiao	In this paper, we propose a Deep Cross-Domain Insurance Recommendation System (DCDIR) for cold start users.
197	A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension	Jie Cai, Zhengzhou Zhu, Ping Nie, Qian Liu	In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e.g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task.
198	Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems	Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Wei Emma Zhang	We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems.
199	Bundle Recommendation with Graph Convolutional Networks	Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, Yong Li	In this work, we propose a graph neural network model named BGCN (short forBundle Graph Convolutional Network ) for bundle recommendation.
200	Re-ranking Answer Selection with Similarity Aggregation	Dong Chen, Shaoliang Peng, Kenli Li, Ying Xu, Jinling Zhang, Xiaolan Xie	In this paper, we propose a similarity aggregation method to rerank the results produced by different baseline neural networks.
201	JIT2R: A Joint Framework for Item Tagging and Tag-based Recommendation	Xu Chen, Changying Du, Xiuqiang He, Jun Wang	In order to take the advantages of such mutually influential signals, we propose to integrate item tagging and tag-based recommendation into a unified model.
202	Attending to Inter-sentential Features in Neural Text Classification	Billy Chiu, Sunil Kumar Sahu, Neha Sengupta, Derek Thomas, Mohammady Mahdy	We investigate graph-level extensions to such models and propose a novel architecture for combining alternative text features.
203	Retrieving Potential Causes from a Query Event	Suchana Datta, Debasis Ganguly, Dwaipayan Roy, Francesca Bonin, Charles Jochim, Mandar Mitra	To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high weights in the topically relevant distribution, leading to potential causal relevance.
204	Bridging Hierarchical and Sequential Context Modeling for Question-driven Extractive Answer Summarization	Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam, Ying Shen	In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization.
205	MGNN: A Multimodal Graph Neural Network for Predicting the Survival of Cancer Patients	Jianliang Gao, Tengfei Lyu, Fan Xiong, Jianxin Wang, Weimao Ke, Zhao Li	In this study, we propose a novel framework for cancer survival prediction named Multimodal Graph Neural Network (MGNN), which explores the features of real-world multimodual data such as gene expression, copy number alteration and clinical data in a unified framework.
206	Deep Neural Matching Models for Graph Retrieval	Kunal Goyal, Utkarsh Gupta, Abir De, Soumen Chakrabarti	In this paper, we present an effective, feature and structure-aware, end-to-end trainable neural match scoring system for graphs.
207	Session-based Recommendation with Hierarchical Leaping Networks	Cheng Guo, Mengfei Zhang, Jinyun Fang, Jiaqi Jin, Mao Pan	In this paper, we propose a Hierarchical Leaping Network (HLN) to explicitly model the users’ multiple preferences by grouping items that share some relationships.
208	Multi-Branch Convolutional Network for Context-Aware Recommendation	Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He	To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer.
209	Predicting Session Length for Product Search on E-commerce Platform	Shashank Gupta, Subhadeep Maji	In this work, we try to early predict the user’s session length, which will enable the control on explore v/s exploit of the search results.
210	Dynamic Link Prediction by Integrating Node Vector Evolution and Local Neighborhood Representation	Xiaorong Hao, Tao Lian, Li Wang	In this paper, we devise a flexible framework for link prediction on dynamic networks regularly archived as different snapshots.
211	Nonintrusive-Sensing and Reinforcement-Learning Based Adaptive Personalized Music Recommendation	Daocheng Hong, Yang Li, Qiwen Dong	Therefore, this paper proposes a novel policy for music recommendation NRRS (Nonintrusive-Sensing and Reinforcement-Learning based Recommender Systems) by integrating prior research streams.
212	Residual-Duet Network with Tree Dependency Representation for Chinese Question-Answering Sentiment Analysis	Guangyi Hu, Chongyang Shi, Shufeng Hao, Yu Bai	In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis.
213	Neural Mathematical Solver with Enhanced Formula Structure	Zhenya Huang, Qi Liu, Weibo Gao, Jinze Wu, Yu Yin, Hao Wang, Enhong Chen	To this end, in this paper, we propose a novel Neural Mathematical Solver (NMS) with enhanced formula structures.
214	Detecting Concept Drift In Medical Triage	Hamish Huggard, Yun Sing Koh, Gillian Dobbie, Edmond Zhang	We introduce a new concept drift detection algorithm for this domain called calibrated drift detection method (CDDM).
215	Evidence Weighted Tree Ensembles for Text Classification	Md Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu, Wei Kang	In this work, we propose to improve the comprehensibility and accuracy of ensemble models by distinguishing word presence and absence.
216	Graph Regularization for Multi-lingual Topic Models	Arnav Kumar Jain, Gundeep Arora, Rahul Agrawal	In this work, we present a novel strategy of creating a pseudo-parallel dataset followed by training topic models for sponsored search retrieval, that also mitigates the short text challenge.
217	Multi-Modal Summary Generation using Multi-Objective Optimization	Anubhav Jangra, Sriparna Saha, Adam Jatowt, Mohammad Hasanuzzaman	In this paper, we propose a novel extractive multi-objective optimization based model to produce a multi-modal summary containing text, images, and videos.
218	A Re-visit of the Popularity Baseline in Recommender Systems	Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li	On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular itemsat the time point when a user interacts with the system.
219	Training Mixed-Objective Pointing Decoders for Block-Level Optimization in Search Recommendation	Harsh Kohli	We propose an architecture that takes all candidate suggestions associated with a given query and outputs a suggestion block.
220	Reinforcement Learning based Recommendation with Graph Convolutional Q-network	Yu Lei, Hongbin Pei, Hanqi Yan, Wenjie Li	To address this limitation, we propose a novel way that builds high-quality graph-structured states/actions according to the user-item bipartite graph.
221	Crowdsourced Text Sequence Aggregation based on Hybrid Reliability and Representation	Jiyi Li	We thus propose a crowdsourced text sequence aggregation method which can capture the hybrid reliability information, i.e., the local question-wise reliability of text answers and global dataset-wise reliability of crowd workers.
222	MRIF: Multi-resolution Interest Fusion for Recommendation	Shihao Li, Dekun Yang, Bufeng Zhang	This paper presents a multi-resolution interest fusion model (MRIF) that takes both properties of users’ interests into consideration.
223	Joint Training Capsule Network for Cold Start Recommendation	Tingting Liang, Congying Xia, Yuyu Yin, Philip S. Yu	This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task.
224	Copula Guided Neural Topic Modelling for Short Texts	Lihui Lin, Hongyu Jiang, Yanghui Rao	In this paper, we focus on adapting the popular Auto-Encoding Variational Bayes based neural topic models to short texts, by exploring the Archimedean copulas to guide the estimated topic distributions derived from linear projected samples of re-parameterized posterior distributions.
225	Soft Kernel-based Ranking on a Statistical Manifold	Xinshi Lin, Wai Lam	We propose a kernel-based neural ranking model based on a statistical manifold.
226	Multi-Level Multimodal Transformer Network for Multimodal Recipe Comprehension	Ao Liu, Shuai Yuan, Chenbin Zhang, Congjian Luo, Yaqing Liao, Kun Bai, Zenglin Xu	To address this challenge, we propose a Multi-Level Multi-Modal Transformer (MLMM-Trans) framework to integrate and understand multiple textual instructions and multiple images.
227	Chinese Document Classification with Bi-directional Convolutional Language Model	Bin Liu, Guosheng Yin	We propose to conduct text classification with such glyph features using bi-directional convolution.
228	Neural Unified Review Recommendation with Cross Attention	Hongtao Liu, Wenjun Wang, Hongyan Xu, Qiyao Peng, Pengfei Jiao	Hence, we propose a unified framework to jointly learn document- and review-level representations of users/items.
229	GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning	Jianfeng Liu, Feiyang Pan, Ling Luo	In this paper, we propose Goal-oriented Chatbots (GoChat), a framework for end-to-end training the chatbot to maximize the long-term return from offline multi-turn dialogue datasets.
230	CapableOf Reasoning: A Step Towards Commonsense Oracle	Jingping Liu, Yanghua Xiao, Ao Wang, Liang He, Bin Shao	In this paper, we propose an online commonsense oracle to achieve knowledge reasoning.
231	Interactive Entity Linking Using Entity-Word Representations	Pei-Chi Lo, Ee-Peng Lim	In this paper, we leverage on human intelligence for embedding-based interactive entity linking.
232	Improving Contextual Language Models for Response Retrieval in Multi-Turn Conversation	Junyu Lu, Xiancong Ren, Yazhou Ren, Ao Liu, Zenglin Xu	In this paper, we propose two approaches to adapt contextual language models in dialogue response selection task.
233	Differentially Private Knowledge Distillation for Mobile Analytics	Lingjuan Lyu, Chi-Hua Chen	To address these problems, we develop a Differentially Private Knowledge Distillation (DPKD) framework to enable on-device deep learning as well as preserve training data privacy.
234	Towards Differentially Private Text Representations	Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao	For the randomization module, we propose a novel local differentially private (LDP) protocol to reduce the impact of privacy parameter ε on accuracy, and provide enhanced flexibility in choosing randomization probabilities for LDP.
235	Large-scale Image Retrieval with Sparse Binary Projections	Changyi Ma, Chonglin Gu, Wenye Li, Shuguang Cui	Following the work along this line, this paper designed a new algorithm which obtains a high-quality sparse binary projection matrix through unsupervised training.
236	Efficiency Implications of Term Weighting for Passage Retrieval	Joel Mackenzie, Zhuyun Dai, Luke Gallagher, Jamie Callan	In this work, we conduct an investigation of query processing efficiency over DeepCT indexes.
237	Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews	Rajdeep Mukherjee, Hari Chandana Peruri, Uppada Vishnu, Pawan Goyal, Sourangshu Bhattacharya, Niloy Ganguly	In this work, we argue the need and propose a solution for generating personalized aspect-based opinion summaries from large collections of online tourist reviews.
238	DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding	Ping Nie, Yuyu Zhang, Xiubo Geng, Arun Ramamurthy, Le Song, Daxin Jiang	To address the efficiency problem, we propose DC-BERT, a decoupled contextual encoding framework that has dual BERT models: an online BERT which encodes the question only once, and an offline BERT which pre-encodes all the documents and caches their encodings.
239	An Intent-guided Collaborative Machine for Session-based Recommendation	Zhiqiang Pan, Fei Cai, Yanxiang Ling, Maarten de Rijke	In this paper, we introduce an Intent-guided Collaborative Machine for Session-based Recommendation (ICM-SR).
240	Rethinking Item Importance in Session-based Recommendation	Zhiqiang Pan, Fei Cai, Yanxiang Ling, Maarten de Rijke	In this paper, we propose a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM, that considers both a user’s long-term and recent behavior in an ongoing session.
241	MHM: Multi-modal Clinical Data based Hierarchical Multi-label Diagnosis Prediction	Zhi Qiao, Zhen Zhang, Xian Wu, Shen Ge, Wei Fan	In this paper, we propose Multi-modal Clinical Data based Hierarchical Multi-label model (MHM) to integrate discrete medical codes, structural information and time series data into the same framework for diagnosis prediction task.
242	How Useful are Reviews for Recommendation? A Critical Review and Potential Improvements	Noveen Sachdeva, Julian McAuley	Through this work, we aim to evaluate the direction in which the field is progressing and encourage robust empirical evaluation.
243	Dual Learning Algorithm for Delayed Conversions	Yuta Saito, Gota Morisihta, Shota Yasui	To overcome these challenges, we propose two unbiased estimators: one for CVR prediction and the other for bias estimation. Subsequently, we propose a dual learning algorithm in which a CVR predictor and a bias estimator are trained in alternating fashion using only observable conversions.
244	Evaluation of Cross Domain Text Summarization	Liam Scanlon, Shiwei Zhang, Xiuzhen Zhang, Mark Sanderson	We examined two state-of-the-art hybrid summarization algorithms from three novel perspectives: we applied them to a form of headline generation not previously tried, we evaluated the generalization of the algorithms by testing them both within and across news domains; and we compared the automatic assessment of the algorithms to human comparative judgments.
245	Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process	Shuanghong Shen, Qi Liu, Enhong Chen, Han Wu, Zhenya Huang, Weihao Zhao, Yu Su, Haiping Ma, Shijin Wang	To this end, in this paper, we propose a novel Convolutional Knowledge Tracing (CKT) method to model individualization in KT.
246	G2T: Generating Fluent Descriptions for Knowledge Graph	Yunzhou Shi, Zhiling Luo, Pengcheng Zhu, Feng Ji, Wei Zhou, Haiqing Chen, Yujiu Yang	In this paper, we propose a novel end-to-end generation model named G2T, which integrates a novel Graph Structure Enhanced Mechanism (GSEM) and a Copy Coverage Loss (CCL).
247	Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection	Chongyang Tao, Wei Wu, Yansong Feng, Dongyan Zhao, Rui Yan	In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.
248	Visual Intents vs. Clicks, Likes, and Purchases in E-commerce	Riku Togashi, Tetsuya Sakai	The present study investigates the relationship between the users’ visual intents (in terms of colour, texture and material, and design) and the amount of user feedback (namely, clicks, likes, and purchases) using real product data and crowdsourcing.
249	MVL: Multi-View Learning for News Recommendation	T.Y.S.S Santosh, Avirup Saha, Niloy Ganguly	In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view.
250	Query/Task Satisfaction and Grid-based Evaluation Metrics Under Different Image Search Intents	Kosetsu Tsukuda, Masataka Goto	In this paper, we investigate this influence by using a publicly available one-month field study dataset.
251	Learning Discriminative Joint Embeddings for Efficient Face and Voice Association	Rui Wang, Xin Liu, Yiu-ming Cheung, Kai Cheng, Nannan Wang, Wentao Fan	In this paper, we present to learn discriminative joint embedding for face-voice association, which can seamlessly train the face subnetwork and voice subnetwork to learn their high-level semantic features, while correlating them to be compared directly and efficiently.
252	TFNet: Multi-Semantic Feature Interaction for CTR Prediction	Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao, Fan Huang	In this paper, we propose a novel Tensor-based Feature interaction Network (TFNet) model, which introduces an operating tensor to elaborate feature interactions via multi-slice matrices in multiple semantic spaces.
253	Investigating Reading Behavior in Fine-grained Relevance Judgment	Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma	To shed light on this research question, we investigate how users allocate their attention to passages of a document during the relevance judgment process.
254	Neural Hierarchical Factorization Machines for User’s Event Sequence Analysis	Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He	In this paper, we consider a two-level structure for capturing the hierarchical information over user’s event sequence: 1) learning effective feature interactions based event representation; 2) modeling the sequence representation of user’s historical events.
255	Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification	Bingbing Xu, Junjie Huang, Liang Hou, Huawei Shen, Jinhua Gao, Xueqi Cheng	In this paper, we propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs.
256	Symmetric Regularization based BERT for Pair-wise Semantic Reasoning	Weidi Xu, Xingyi Cheng, Kunlong Chen, Taifeng Wang	To remedy this, we propose to augment the NSP task to a multi-class categorization task, which includes previous sentence prediction (PSP).
257	Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction	Weinan Xu, Hengxu He, Minshi Tan, Yunming Li, Jun Lang, Dongbai Guo	We therefore propose an improvement over DIN to model arbitrary interest hierarchy: Deep Interest with Hierarchical Attention Network (DHAN).
258	A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention	Deqing Yang, Zengchun Song, Lvxin Xue, Yanghua Xiao	In this paper, we propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute-level to achieve performance gains.
259	Multi-source Domain Adaptation for Sentiment Classification with Granger Causal Inference	Min Yang, Ying Shen, Xiaojun Chen, Chengming Li	In this paper, we propose a multi-source domain adaptation method with a Granger-causal objective (MDA-GC) for cross-domain sentiment classification.
260	TADS: Learning Time-Aware Scheduling Policy with Dyna-Style Planning for Spaced Repetition	Zhengyu Yang, Jian Shen, Yunfei Liu, Yang Yang, Weinan Zhang, Yong Yu	In this paper, we aim to learn a scheduling policy that fully exploits the varying time interval information with high sample efficiency.
261	TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation	Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan	In this paper, we propose a novel target attentive graph neural network (TAGNN) model for session-based recommendation.
262	Query Classification with Multi-objective Backoff Optimization	Hang Yu, Lester Litchfield	To fill up the research gap, we propose the Query Classification with Multi-objective Backoff (QCMB).
263	Influence Function for Unbiased Recommendation	Jiangxing Yu, Hong Zhu, Chih-Yao Chang, Xinhua Feng, Bowen Yuan, Xiuqiang He, Zhenhua Dong	Inspired by the sample reweight work for robust deep learning, we propose a novel influence function based method for recommendation modeling, and analyze how the influence function corrects the bias.
264	Few-Shot Generative Conversational Query Rewriting	Shi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong, Paul Bennett, Jianfeng Gao, Zhiyuan Liu	This paper presents a few-shot generative approach to conversational query rewriting.
265	User-Inspired Posterior Network for Recommendation Reason Generation	Haolan Zhan, Hainan Zhang, Hongshen Chen, Lei Shen, Yanyan Lan, Zhuoye Ding, Dawei Yin	Therefore, in this paper, we consider generating the recommendation reason by taking into account not only the product attributes but also the customer-generated product QA discussions.
266	An Analysis of BERT in Document Ranking	Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma	To increase the explainability of the ranking process performed by BERT, we investigate a state-of-the-art BERT-based ranking model with focus on its attention mechanism and interaction behavior.
267	Read, Attend, and Exclude: Multi-Choice Reading Comprehension by Mimicking Human Reasoning Process	Chenbin Zhang, Congjian Luo, Junyu Lu, Ao Liu, Bing Bai, Kun Bai, Zenglin Xu	To effectively comprehend the context and select the correct answer from different perspectives, we propose the Read-Attend-Exclude (RAE) model which is motivated by what human readers do for MCRC in multi-rounds reasoning process.
268	SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression	Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari	In this paper, we propose SummPip: an unsupervised method for multi-document summarization, in which we convert the original documents to a sentence graph, taking both linguistic and deep representation into account, then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary.
269	Improving Neural Chinese Word Segmentation with Lexicon-enhanced Adaptive Attention	Xiaoyan Zhao, Min Yang, Qiang Qu, Yang Sun	In this paper, we propose a lexicon-enhanced adaptive attention network (LAAN), which takes full advantage of external lexicons to deal with the rare or ambiguous words.
270	Sentiment-guided Sequential Recommendation	Lin Zheng, Naicheng Guo, Weihao Chen, Jin Yu, Dazhi Jiang	To investigate the influence of temporal sentiments on user preferences, we propose generating preferences by guiding user behavior through sequential sentiments.
271	L2R²: Leveraging Ranking for Abductive Reasoning	Yunchang Zhu, Liang Pang, Yanyan Lan, Xueqi Cheng	With this new perspective, a novel L2R2 approach is proposed under the learning-to-rank framework.
272	Automatic Generation of Topic Labels	Areej Alokaili, Nikolaos Aletras, Mark Stevenson	This paper proposes using a sequence-to-sequence neural-based approach to generate labels that does not suffer from this limitation.
273	Extractive Snippet Generation for Arguments	Milad Alshomary, Nick Düsterhus, Henning Wachsmuth	In this paper, we introduce the task of generating a snippet that represents the main claim and reason of an argument.
274	Proposal and Comparison of Health Specific Features for the Automatic Assessment of Readability	Hélder Antunes, Carla Teixeira Lopes	In this work, we explore methods to assess the readability of health content automatically.
275	MarkedBERT: Integrating Traditional IR Cues in Pre-trained Language Models for Passage Retrieval	Lila Boualili, Jose G. Moreno, Mohand Boughanem	To study the effectiveness of this assumption, we propose MarkedBERT a modified version of one of the most popular pre-trained models via language modeling tasks, BERT.
276	Relevance Models for Multi-Contextual Appropriateness in Point-of-Interest Recommendation	Anirban Chakraborty, Debasis Ganguly, Owen Conlan	We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts.
277	CAsT-19: A Dataset for Conversational Information Seeking	Jeffrey Dalton, Chenyan Xiong, Vaibhav Kumar, Jamie Callan	CAsT-19 is a new dataset that supports research on conversational information seeking.
278	Predicting Perceptual Speed from Search Behaviour	Olivia Foulds, Alessandro Suglia, Leif Azzopardi, Martin Halvey	Consequently, this paper evaluated whether PS can be automatically classified from search behaviour using several machine learning models trained on features extracted from TREC Common Core search task logs.
279	Learning Term Discrimination	Jibril Frej, Philippe Mulhem, Didier Schwab, Jean-Pierre Chevallet	In this work, we propose to learn TDVs for document indexing with shallow neural networks that approximate traditional IR ranking functions such as TF-IDF and BM25.
280	Sampling Bias Due to Near-Duplicates in Learning to Rank	Maik Fröbe, Janek Bevendorff, Jan Heinrich Reimer, Martin Potthast, Matthias Hagen	We investigate for the first time the effect of a sampling bias on LTR~models due to the potential presence of near-duplicate web pages in the training data, and how (in)consistent relevance feedback of duplicates influences an LTR~model’s decisions.
281	Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs	Lu Gan, Diana Nurbakova, Léa Laporte, Sylvie Calabretto	In this paper, we focus on enhancing diversity of top-N recommendation, while ensuring the trade-off between accuracy and diversity.
282	Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback	Carlos Gemmell, Federico Rossetto, Jeffrey Dalton	In this work we introduce and study modern Transformer architectures for this task.
283	Unsupervised Semantic Hashing with Pairwise Reconstruction	Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma	Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model.
284	Factuality Checking in News Headlines with Eye Tracking	Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Birger Larsen, Stephen Alstrup, Christina Lioma	We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines.
285	ArTest: The First Test Collection for Arabic Web Search with Relevance Rationales	Maram Hasanain, Yassmine Barkallah, Reem Suwaileh, Mucahid Kutlu, Tamer Elsayed	In this work, we present ArTest, the first large-scale test collection designed for the evaluation of ad-hoc search over the Arabic Web.
286	Local Self-Attention over Long Text for Efficient Document Retrieval	Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra, Nick Craswell, Allan Hanbury	In this work, we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window.
287	Using Exploration to Alleviate Closed Loop Effects in Recommender Systems	Amir H. Jadidinejad, Craig Macdonald, Iadh Ounis	In this paper, we first introduce the closed loop feedback and then investigate the effect of closed loop feedback in both the training and offline evaluation of recommendation models, in contrast to a further exploration of the users’ preferences (obtained from the randomly presented items).
288	A Heterogeneous Graph Neural Model for Cold-start Recommendation	Siwei Liu, Iadh Ounis, Craig Macdonald, Zaiqiao Meng	In this paper, we propose a new recommendation model, named Heterogeneous Graph Neural Recommender (HGNR), to tackle the cold-start problem while ensuring effective recommendations for all users.
289	Query-level Early Exit for Additive Learning-to-Rank Ensembles	Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani	In this paper, we investigate the novel problem of query-level early exiting, aimed at deciding the profitability of early stopping the traversal of the ranking ensemble for all the candidate documents to be scored for a query, by simply returning a ranking based on the additive scores computed by a limited portion of the ensemble.
290	Segmenting Search Query Logs by Learning to Detect Search Task Boundaries	Luis Lugo, Jose G. Moreno, Gilles Hubert	Therefore, we propose a model for sequential search log segmentation.
291	A Multilingual Approach for Unsupervised Search Task Identification	Luis Lugo, Jose G. Moreno, Gilles Hubert	Hence, we propose an unsupervised multilingual approach for search task identification.
292	Sequential-based Adversarial Optimisation for Personalised Top-N Item Recommendation	Jarana Manotumruksa, Emine Yilmaz	To address this challenge, we propose a Sequential-based Adversarial Optimisation (SAO) framework that effectively enhances the generalisation of sequential-based factorised approaches.
293	CrossBERT: A Triplet Neural Architecture for Ranking Entity Properties	Jarana Manotumruksa, Jeff Dalton, Edgar Meij, Emine Yilmaz	In this work we tackle the task of automatically constructing actionable knowledge graphs in response to a user query in order to support a wider variety of increasingly complex assistant tasks.
294	Active Learning Stopping Strategies for Technology-Assisted Sensitivity Review	Graham McDonald, Craig Macdonald, Iadh Ounis	In this work, we propose two active learning stopping strategies for technology-assisted sensitivity review.
295	Topic Propagation in Conversational Search	Ida Mele, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Ophir Frieder	We present a comprehensive experimental evaluation of the architecture assessed in terms of traditional IR metrics at small cutoffs.
296	Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities	Guilherme Ramos, Ludovico Boratto	In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences.
297	Do Neural Ranking Models Intensify Gender Bias?	Navid Rekabsaz, Markus Schedl	In this work, we examine various recent IR models from the perspective of the degree of gender bias in their retrieval results.
298	The Curious Case of IR Explainability: Explaining Document Scores within and across Ranking Models	Procheta Sen, Debasis Ganguly, Manisha Verma, Gareth J.F. Jones	We propose a general methodology of approximating an IR model as the coefficients of a linear function of these three fundamental aspects (and an additional aspect of semantic similarity between terms for neural models), which potentially can help IR practitioners to optimize the relative importance of each aspect on specific document collection and types of queries.
299	Fair Classification with Counterfactual Learning	Maryam Tavakol	In this paper, we design a counterfactual framework to model fairness-aware learning which benefits from counterfactual reasoning to achieve more fair decision support systems.
300	Multi-grouping Robust Fair Ranking	Thibaut Thonet, Jean-Michel Renders	Therefore, in this paper, we study the problem of designing fair ranking algorithms without knowing in advance the groupings that will be used later to assess their fairness.
301	Distilling Knowledge for Fast Retrieval-based Chat-bots	Amir Vakili Tahami, Kamyar Ghajar, Azadeh Shakery	In this paper, we propose a new cross-encoder architecture and transfer knowledge from this model to a bi-encoder model using distillation.
302	An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues	Svitlana Vakulenko, Evangelos Kanoulas, Maarten de Rijke	We propose a set of unsupervised metrics, termed ConversationShape, that highlights the role each of the conversation participants plays by comparing the distribution of vocabulary and utterance types.
303	Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank	Ali Vardasbi, Maarten de Rijke, Ilya Markov	In this paper, we propose a propensity estimation method for the cascade scenario, called cascade model-based inverse propensity scoring (CM-IPS).
304	Studying Ranking-Incentivized Web Dynamics	Ziv Vasilisky, Moshe Tennenholtz, Oren Kurland	We present an initial such dataset that is based on TREC’s ClueWeb09 dataset.
305	How UMass-FSD Inadvertently Leverages Temporal Bias	Dominik Wurzer, Yumeng Qin	Our analysis reveals an increased contribution of temporally distant documents, resulting from an unusual way of handling incremental term statistics.
306	On the Reliability of Test Collections for Evaluating Systems of Different Types	Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Daniel Campos	This paper uses simulated pooling to test the fairness and reusability of test collections, showing that especially when shallow pools (e.g. depth-10 pools) are used, pooling based on traditional systems only may lead to biased evaluation of deep learning systems.
307	KLOOS: KL Divergence-based Out-of-Scope Intent Detection in Human-to-Machine Conversations	Eyup Halit Yilmaz, Cagri Toraman	We propose an out-of-scope intent detection method, called KLOOS, based on a novel feature extraction mechanism that incorporates the information accumulation of sequential word processing.
308	DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts	Markus Zlabinger, Marta Sabou, Sebastian Hofstätter, Mete Sertkan, Allan Hanbury	To overcome these limitations, we propose to support workers in addition to task-level examples, also with "task-instance level" examples that are semantically similar to the currently annotated data sample (referred to as Dynamic Examples for Annotation, DEXA).
309	A Lightweight Environment for Learning Experimental IR Research Practices	Zeynep Akkalyoncu Yilmaz, Charles L. A. Clarke, Jimmy Lin	As an initial attempt to address these issues, we describe materials that we have developed for the "Introduction to IR" session at the ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search (AFIRM 2020), which builds on three components: the open-source Lucene search library, cloud-based notebooks, and the MS MARCO dataset.
310	BRENDA: Browser Extension for Fake News Detection	Bjarte Botnevik, Eirik Sakariassen, Vinay Setty	In this demonstration, we propose BRENDA a browser extension which can be used to automate the entire process of credibility assessments of false claims.
311	Agent Dialogue: A Platform for Conversational Information Seeking Experimentation	Adam Czyzewski, Jeffrey Dalton, Anton Leuski	In this demo we present the Agent Dialogue (AD) platform, an open-source system developed for researchers to perform Wizard-of-Oz CIS experiments.
312	DataMirror: Reflecting on One’s Data Self: A Tool for Social Media Users to Explore Their Digital Footprints	Amal Htait, Leif Azzopardi, Emma Nicol, Wendy Moncur	In this paper, we present DataMirror, an initial prototype tool, that enables social network users to aggregate their online data so that they can search, browse and visualise what they have put online.
313	Conversational Question Answering over Passages by Leveraging Word Proximity Networks	Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum	In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns.
314	FigExplorer: A System for Retrieval and Exploration of Figures from Collections of Research Articles	Saar Kuzi, ChengXiang Zhai, Yin Tian, Haichuan Tang	In this paper, we present FigExplorer, a novel general system that supports the retrieval and exploration of research article figures.
315	APS: An Active PubMed Search System for Technology Assisted Reviews	Dan Li, Panagiotis Zafeiriadis, Evangelos Kanoulas	In this work, we built an online active search system for systematic reviews, named APS, by applying an state-of-the-art TAR approach — Continuous Active Learning.
316	Systematic Review Automation Tools for End-to-End Query Formulation	Hang Li, Harrisen Scells, Guido Zuccon	In this paper, we present a novel end-to-end set of advanced tools for information specialists.
317	Deep Job Understanding at LinkedIn	Shan Li, Baoxu Shi, Jaewon Yang, Ji Yan, Shuai Wang, Fei Chen, Qi He	In this demonstration, we present LinkedIn’s job posting flow and demonstrate how the integrated deep job understanding work improves job posters’ satisfaction and provides significant metric lifts in LinkedIn’s job recommendation system.
318	Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format	Jimmy Lin, Joel Mackenzie, Chris Kamphuis, Craig Macdonald, Antonio Mallia, Michał Siedlaczek, Andrew Trotman, Arjen de Vries	Overall, we recommend CIFF as a low-effort approach to support independent innovation while enabling the types of fair evaluations that are critical for driving the field forward.
319	Web of Scholars: A Scholar Knowledge Graph	Jiaying Liu, Jing Ren, Wenqing Zheng, Lianhua Chi, Ivan Lee, Feng Xia	In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science.
320	SPot: A Tool for Identifying Operating Segments in Financial Tables	Zhiqiang Ma, Steven Pomerville, Mingyang Di, Armineh Nourbakhsh	In this paper we present SPot, an automated tool for detecting operating segments and their related performance indicators from earnings reports.
321	Receptor: A Platform for Exploring Latent Relations in Sensitive Documents	Hitarth Narvala, Graham McDonald, Iadh Ounis	The system provides novel scalable graph search and exploration functionalities as well as interactive visualisations of the latent relations between related entities, events, and documents to enable users to identify hidden patterns of sensitivity.
322	FactCatch: Incremental Pay-as-You-Go Fact Checking with Minimal User Effort	Thanh Tam Nguyen, Matthias Weidlich, Hongzhi Yin, Bolong Zheng, Quang Huy Nguyen, Quoc Viet Hung Nguyen	This paper presents FactCatch, a human-in-the-loop system to guide users in fact checking that aims at minimisation of the invested effort.
323	WIRE: An Automated Report Generation System using Topical and Temporal Summarization	Yunseok Noh, Yongmin Shin, Junmo Park, A-Yeong Kim, Su Jeong Choi, Hyun-Je Song, Seong-Bae Park, Seyoung Park	To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics.
324	Experimaestro and Datamaestro: Experiment and Dataset Managers (for IR)	Benjamin Piwowarski	In this demo paper, we present two managers, Experimaestro and Datamaestro, and their add-ons for IR, designed to help to define and run experimental plans.
325	QuAChIE: Question Answering based Chinese Information Extraction System	Dongyu Ru, Zhenghui Wang, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, Yong Yu	In this paper, we present the design of QuAChIE, a Question Answering based Chinese Information Extraction system. For the training and evaluation of each model in the system, we build a large-scale information extraction dataset using Wikidata and Wikipedia pages by distant supervision.
326	Vis-Trec: A System for the In-depth Analysis of trec_eval Results	Mahtab Tamannaee, Negar Arabzadeh, Ebrahim Bagheri	We introduce Vis-Trec, an open-source cross-platform system, which provides the capability to perform in-depth analysis of the results obtained from trec-style evaluation campaigns.
327	JASSjr: The Minimalistic BM25 Search Engine for Teaching and Learning Information Retrieval	Andrew Trotman, Kat Lilly	We present JASSjr, a minimalistic trec_eval compatible BM25-ranking search engine that can index small TREC data sets such as the Wall Street Journal collection.
328	ServiceGroup: A Human-Machine Cooperation Solution for Group Chat Customer Service	Minghui Yang, Hengbin Cui, Shaosheng Cao, Yafang Wang, Xiaolong Li	To improve efficiency, we propose a human-machine cooperation solution called ServiceGroup, where relevant agents and customers are invited into the same group, and the system can provide a series of intelligent functions, including question notification, question recommendation and knowledge extraction.
329	Macaw: An Extensible Conversational Information Seeking Platform	Hamed Zamani, Nick Craswell	This paper introduces Macaw, an open-source framework with a modular architecture for CIS research.
330	REL: An Entity Linker Standing on the Shoulders of Giants	Johannes M. van Hulst, Faegheh Hasibi, Koen Dercksen, Krisztian Balog, Arjen P. de Vries	The REL system presented in this paper aims to fill that gap.
331	GMCM: Graph-based Micro-behavior Conversion Model for Post-click Conversion Rate Estimation	Wentian Bao, Hong Wen, Sha Li, Xiao-Yang Liu, Quan Lin, Keping Yang	We propose a novel CVR model, namely, Graph-based Micro-behavior Conversion Model (GMCM), that utilizes Graph Convolutional networks (GCN) to enhance the conventional CVR modeling.
332	A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users	Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, Jing Xiao	In this paper, we propose a novel framework called a Heterogeneous information network based Cross Domain Insurance Recommendation (HCDIR) system for cold start users.
333	Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments	Dawei Cheng, Fangzhou Yang, Xiaoyang Wang, Ying Zhang, Liqing Zhang	Therefore, in this paper, we present a knowledge graph-based event embedding framework for quantitative investments.
334	ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation	Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, Wenwu Ou	In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG.
335	Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to Intervene Novel Coronavirus COVID-19 Outbreak	Zhenxin Fu, Yu Wu, Hailei Zhang, Yichuan Hu, Dongyan Zhao, Rui Yan	In this paper, we aim at drawing lessons from the COVID-19 outbreak process in China and using the experiences to help the interventions against the coronavirus wherever in need.
336	FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval	Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Peng Li, Yi Wei, Yi Hu, Hao Wang	In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.
337	Understanding Echo Chambers in E-commerce Recommender Systems	Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang	In this paper, we aim to analyze the echo chamber phenomenon in Alibaba Taobao — one of the largest e-commerce platforms in the world.
338	Efficient and Effective Query Auto-Completion	Simon Gog, Giulio Ermanno Pibiri, Rossano Venturini	In this work we describe the implementation that empowers a new QAC system at eBay, and discuss its efficiency/effectiveness in relation to other approaches at the state-of-the-art.
339	Efficient Image Gallery Representations at Scale Through Multi-Task Learning	Benjamin Gutelman, Pavel Levin	We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks.
340	A Counterfactual Framework for Seller-Side A/B Testing on Marketplaces	Viet Ha-Thuc, Avishek Dutta, Ren Mao, Matthew Wood, Yunli Liu	To overcome this challenge, we propose a counterfactual framework for seller-side A/B testing.
341	Think Beyond the Word: Understanding the Implied Textual Meaning by Digesting Context, Local, and Noise	Guoxiu He, Zhe Gao, Zhuoren Jiang, Yangyang Kang, Changlong Sun, Xiaozhong Liu, Wei Lu	In this study, inspired by human reading comprehension, we propose a novel, simple, and effective deep neural framework, called Skim and Intensive Reading Model (SIRM), for figuring out implied textual meaning.
342	Automated Embedding Size Search in Deep Recommender Systems	Haochen Liu, Xiangyu Zhao, Chong Wang, Xiaobing Liu, Jiliang Tang	In this paper, we propose to dynamically search the embedding sizes for different users and items and introduce a novel embedding size adjustment policy network (ESAPN).
343	Network On Network for Tabular Data Classification in Real-world Applications	Yuanfei Luo, Hao Zhou, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang	In this paper, we present Network On Network (NON), a practical tabular data classification model based on deep neural network to provide accurate predictions.
344	Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach	Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He	In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
345	Large Scale Abstractive Multi-Review Summarization (LSARS) via Aspect Alignment	Haojie Pan, Rongqin Yang, Xin Zhou, Rui Wang, Deng Cai, Xiaozhong Liu	We propose the first large-scale abstractive multi-review summarization dataset that leverages more than 17.9 billion raw reviews and uses novel aspect-alignment techniques based on aspect annotations.
346	User Behavior Retrieval for Click-Through Rate Prediction	Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, Yong Yu	To tackle these issues, in this paper we consider it from the data perspective instead of just designing more sophisticated yet complicated models and propose User Behavior Retrieval for CTR prediction (UBR4CTR) framework.
347	Identifying Tasks from Mobile App Usage Patterns	Yuan Tian, Ke Zhou, Mounia Lalmas, Dan Pelleg	We focus on two problems: (i) given a sequential pair of app usage logs, identify if there exists a task boundary, and (ii) given any pair of two app usage logs, identify if they belong to the same task.
348	Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models	Mengxi Wei, YIfan He, Qiong Zhang	We study the problem of information extraction from visually rich documents (VRDs) and present a model that combines the power of large pre-trained language models and graph neural networks to efficiently encode both textual and visual information in business documents.
349	Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition for Conversion Rate Prediction	Hong Wen, Jing Zhang, Yuan Wang, Fuyu Lv, Wentian Bao, Quan Lin, Keping Yang	Observing that users always take several purchase-related actions after clicking, we propose a novel idea of post-click behavior decomposition.
350	How Airbnb Tells You Will Enjoy Sunset Sailing in Barcelona? Recommendation in a Two-Sided Travel Marketplace	Liang Wu, Mihajlo Grbovic	In this work, we present our efforts on building a recommender system for Airbnb Experiences, a two-sided online marketplace for tours and activities.
351	Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network	Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, Liefeng Bo	To tackle the above challenge, this work proposes a Memory-Augmented Transformer Networks (MATN), to enable the recommendation with multiplex behavioral relational information, and joint modeling of type-specific behavioral context and type-wise behavior inter-dependencies, in a fully automatic manner.
352	Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning	Han Zhang, Songlin Wang, Kang Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Weipeng Yan, Wen-Yun Yang	In this paper, we present a novel approach called DPSR, which stands for Deep Personalized and Semantic Retrieval, to tackle this problem.
353	Searching the Web for Cross-lingual Parallel Data	Ahmed El-Kishky, Philipp Koehn, Holger Schwenk	We introduce techniques for searching for cross-lingual parallel data based on language, content, and other metadata.
354	Recent Advances in Conversational Information Retrieval	Jianfeng Gao, Chenyan Xiong, Paul Bennett	This tutorial presents recent advances in CIR, focusing mainly on neural approaches and new applications developed in the past five years.
355	Conversational Recommendation: Formulation, Methods, and Evaluation	Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua	This tutorial covers these four directions, providing a review of existing approaches and progress on the topic.
356	Reciprocal Recommendation: Matching Users with the Right Users	Iván Palomares	This tutorial introduces the emerging and novel topic of reciprocal recommender systems, by analyzing their information retrieval, data-driven preference modelling and integration mechanisms for predicting suitable user matches.
357	Question Answering over Curated and Open Web Sources	Rishiraj Saha Roy, Avishek Anand	This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used.
358	Tutorial on Task-Based Search and Assistance	Chirag Shah, Ryen W. White	This tutorial will introduce the attendees to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching).
359	Modeling User Behavior for Vertical Search: Images, Apps and Products	Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke	This tutorial will introduce the research and applications of user behavior modeling for vertical search.
360	Interactive Information Retrieval: Models, Algorithms, and Evaluation	ChengXiang Zhai	This tutorial systematically reviews the progress of research in IIR with an emphasis on the most recent progress in the development of models, algorithms, and evaluation strategies for IIR.
361	AIIS: The SIGIR 2020 Workshop on Applied Interactive Information Systems	Hongshen Chen, Zhaochun Ren, Pengjie Ren, Dawei Yin, Xiaodong He	We aim to discuss the issues of applying interactive information models to production systems, as well as to shed some light on the fundamental characteristics, i.e., interactivity and applicability, of different interactive tasks.
362	FinIR 2020: The First Workshop on Information Retrieval in Finance	Fuli Feng, Cheng Luo, Xiangnan He, Yiqun Liu, Tat-Seng Chua	The workshop aims to bring together a diverse set of researchers and practitioners interested in investigating relevant topics.
363	BIRDS – Bridging the Gap between Information Science, Information Retrieval and Data Science	Ingo Frommholz, Haiming Liu, Massimo Melucci	The BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS).
364	ECOM’20: The SIGIR 2020 Workshop on eCommerce	Dietmar Jannach, Surya Kallumadi, Tracy Holloway King, Weihua Luo, Shervin Malmasi	This workshop (1) brings together researchers and practitioners of eCommerce IR to discuss topics unique to it, (2) determines how to use eCommerce’s unique combination of free text, structured data, and customer behavioral data to improve search relevance, and (3) examines how to build data sets and evaluate algorithms in this domain.
365	Deep Natural Language Processing for Search and Recommendation	Bo Long, Jieping Ye, Zang Li, Huiji Gao, Sandeep Kumar Jha	In this workshop, we propose to discuss deep neural network based NLP technologies and their applications in search and recommendation, with the goal of understanding (1) Why deep NLP is helpful; (2) What are the challenges to develop and productionize it; (3) How to overcome the challenges; (4) Where deep NLP models produce the largest impact.
366	Legal Intelligence: Algorithmic, Data, and Social Challenges	Changlong Sun, Yating Zhang, Xiaozhong Liu, Fei Wu	The objective of this workshop is to aggregate studies/applications of text mining/retrieval and NLP automation in the context of classical/novel legal tasks, which address algorithmic, data and social challenges of legal intelligence.
367	Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances	Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Hui Yang, Alex Beutel	Our workshop aims to provide a venue, which can bring together academia researchers and industry practitioners (i) to discuss the principles, limitations and applications of DRL for information retrieval, and (ii) to foster research on innovative algorithms, novel techniques, and new applications of DRL to information retrieval.
368	EARS 2020: The 3rd International Workshop on ExplainAble Recommendation and Search	Yongfeng Zhang, Xu Chen, Yi Zhang, Min Zhang, Chirag Shah	The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks.
369	Language Agnostic Hate Speech Detection	Aymé Arango	The main research proposal of this thesis is to characterize hate speech and other forms of online harassment from different perspectives and use this characterizations to create novel models for online hate speech detection across different languages and domains.
370	Legal Data Analytics: Developing Assistive Tools for Legal Practitioners	Paheli Bhattacharya	We outline the challenges and some initial progress made on each of them.
371	Multi-Document Answer Generation for Non-Factoid Questions	Valeriia Baranova-Bolotova	The current research will be devoted to the challenging and under-investigated task of multi-source answer generation for complex non-factoid questions.
372	Spatio-temporal Conditioned Language Models	Juglar Diaz	We aim to develop neural network models for language modeling conditioned on spatio-temporal variables with the ability to capture properties such as: neighborhood, periodicity and hierarchy.
373	Explaining Recommendations in Heterogeneous Networks	Azin Ghazimatin	Explaining Recommendations in Heterogeneous Networks
374	When the Human is in the Loop: Cost, Effort and Behavior	Lei Han	The aim of this research is to study how these tasks are completed, to help reduce non-essential data collection costs, and to support workers in efficient task completion.
375	End-to-End Contextualized Document Indexing and Retrieval with Neural Networks	Sebastian Hofstätter	End-to-End Contextualized Document Indexing and Retrieval with Neural Networks
376	Incorporating User Feedback in Conversational Question Answering over Heterogeneous Web Sources	Magdalena Kaiser	This PhD thesis will explore conversational question answering with a special emphasis on incorporating user feedback.
377	User Modeling Towards Stateful Learning to Rank	Yanpeng Lin	In this research proposal, we focus on product search in E-commerce.
378	Enhancing Graph Neural Networks for Recommender Systems	Siwei Liu	In this work, we propose to enhance the performance of the GBRS approaches along several research directions, namely leveraging additional items and users’ side information, extending the existing undirected graphs to account for social influence among users, and enhancing their underlying optimisation criterion.
379	Towards Legal Case Retrieval	Yunqiu Shao	As a primary attempt, we propose to develop a retrieval model to tackle these challenges based on the benchmarks in this task.
380	Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine	Zuoxi Yang	In this work, we propose a neural-based biomedical information retrieval model to address the semantic gap problem and fully investigate the utility of KG for the explainable biomedical information retrieval systems.
381	Towards Evaluating Veracity of Textual Statements on the Web	Qiang Zhang	The major contributions to this growing area of research will be made from the following aspects: (1) improve stance detection and incorporate it to misinformation detection; (2) effectively utilize noisy, unstructured user engagements on social media platforms; (3) design a general framework for the early misinformation detection.
382	Bridging East and West: An Integration of TCM and Western Medicine in Medical Text Mining	Runjie Zhu	The proposed dissertation research aims to bridge eastern and western medical philosophies by applying named entity recognition (NER) and information retrieval (IR) models supported by medical and cross lingual knowledge graphs, to enhance the retrieval performance as well as to increase the model explainability.