Paper Digest: SIGIR 2019 Highlights

July 19, 2019October 30, 2019 admin

Download SIGIR-2019-Paper-Digests.pdf– highlights of all SIGIR-2019 papers.

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 2019, it is to be held in Paris, France. There were 426 paper submissions, of which 84 were accepted as long papers.

To help AI 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 / summary to quickly get the main idea of each paper.

We thank all authors for writing these interesting papers, and readers for reading our digests. If you do not want to miss any interesting AI paper, you are welcome to sign up our free paper digest service to get new paper updates customized to your own interests on a daily basis.

Paper Digest Team
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TABLE 1: SIGIR 2019 Papers

	Title	Authors	Highlight
1	A General Framework for Counterfactual Learning-to-Rank	Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, Thorsten Joachims	Going beyond this special case, this paper provides a general and theoretically rigorous framework for counterfactual learning-to-rank that enables unbiased training for a broad class of additive ranking metrics (e.g., Discounted Cumulative Gain (DCG)) as well as a broad class of models (e.g., deep networks).
2	To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions	Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke	In this study we provide the first benchmarking of both counterfactual and online LTR methods under different experimental conditions.
3	Domain Adaptation for Enterprise Email Search	Brandon Tran, Maryam Karimzadehgan, Rama Kumar Pasumarthi, Michael Bendersky, Donald Metzler	To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise.
4	Health Cards for Consumer Health Search	Jimmy Jimmy, Guido Zuccon, Bevan Koopman, Gianluca Demartini	This paper investigates the impact of health cards in consumer health search (CHS) – people seeking health advice online.
5	Hate Speech Detection is Not as Easy as You May Think: A Closer Look at Model Validation	Aym? Arango, Jorge P?rez, Barbara Poblete	In this work, we analyze this apparent contradiction between existing literature and actual applications.
6	Harvesting Drug Effectiveness from Social Media	Zi Chai, Xiaojun Wan, Zhao Zhang, Minjie Li	In this paper, we propose a model regarding mention pairs as nodes connected by multiple types of edges. To this end, we build a dataset containing 25K tweets describing drug use, and further harvest drug effectiveness by performing Relation Extraction (RE) between chemicals and diseases.
7	Generic Intent Representation in Web Search	Hongfei Zhang, Xia Song, Chenyan Xiong, Corby Rosset, Paul N. Bennett, Nick Craswell, Saurabh Tiwary	This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search.
8	Quantifying and Alleviating the Language Prior Problem in Visual Question Answering	Yangyang Guo, Zhiyong Cheng, Liqiang Nie, Yibing Liu, Yinglong Wang, Mohan Kankanhalli	In this paper, we make contributions to solve the above problems from two perspectives.
9	Document Gated Reader for Open-Domain Question Answering	Bingning Wang, Ting Yao, Qi Zhang, Jingfang Xu, Zhixing Tian, Kang Liu, Jun Zhao	In this paper, we propose a document gated reader to generate the right answer from multiple documents.
10	Adaptive Multi-Attention Network Incorporating Answer Information for Duplicate Question Detection	Di Liang, Fubao Zhang, Weidong Zhang, Qi Zhang, Jinlan Fu, Minlong Peng, Tao Gui, Xuanjing Huang	In this paper, we propose an answer information- enhanced adaptive multi-attention network (AMAN) to perform this task.
11	Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs	Xiaolu Lu, Soumajit Pramanik, Rishiraj Saha Roy, Abdalghani Abujabal, Yafang Wang, Gerhard Weikum	This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents.
12	Controlling Risk of Web Question Answering	Lixin Su, Jiafeng Guo, Yixin Fan, Yanyan Lan, Xueqi Cheng	In this work, we first conduct an in-depth investigation over the risk of Web QA. We then introduce a novel risk control framework, which consists of a qualify model for uncertainty estimation using the probe idea, and a decision model for selectively output.
13	Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation	Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon Jose	In this work, we propose Relational Collaborative Filtering (RCF) to exploit multiple item relations in recommender systems.
14	Noise Contrastive Estimation for One-Class Collaborative Filtering	Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai	To address this deficiency, we leverage insights from Noise Contrastive Estimation (NCE) to derive a closed-form, efficiently computable “depopularized” embedding.
15	Compositional Coding for Collaborative Filtering	Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Chenghao, Jianling Sun, Steven C.H. Hoi	In this work, we attempt to improve the efficiency without hurting the model performance by utilizing both the accuracy of real-valued vectors and the efficiency of binary codes to represent users/items.
16	Unified Collaborative Filtering over Graph Embeddings	Pengfei Wang, Hanxiong Chen, Yadong Zhu, Huawei Shen, Yongfeng Zhang	In this paper, we propose a Unified Collaborative Filtering framework based on Graph Embeddings (UGrec for short) to solve the problem.
17	Neural Graph Collaborative Filtering	Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua	In this work, we propose to integrate the user-item interactions – more specifically the bipartite graph structure – into the embedding process.
18	Knowledge Tracing with Sequential Key-Value Memory Networks	Ghodai Abdelrahman, Qing Wang	In this paper, we address these limitations by proposing a novel deep learning model for knowledge tracing, namely Sequential Key-Value Memory Networks (SKVMN).
19	Personal Knowledge Base Construction from Text-based Lifelogs	An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen	In this paper, we aim to extract life events from textual data shared on Twitter and construct personal knowledge bases of individuals.
20	Identifying Entity Properties from Text with Zero-shot Learning	Wiradee Imrattanatrai, Makoto P. Kato, Masatoshi Yoshikawa	We propose a method for identifying a set of entity properties from text.
21	One-Class Order Embedding for Dependency Relation Prediction	Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo	We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data.
22	ENT Rank: Retrieving Entities for Topical Information Needs through Entity-Neighbor-Text Relations	Laura Dietz	Related work has demonstrated the helpfulness of utilizing information about entities in text retrieval; here we explore the converse: Utilizing information about text in entity retrieval.
23	An Efficient Adaptive Transfer Neural Network for Social-aware Recommendation	Chong Chen, Min Zhang, Chenyang Wang, Weizhi Ma, Minming Li, Yiqun Liu, Shaoping Ma	To address the above problems, we propose an Efficient Adaptive Transfer Neural Network (EATNN).
24	A Neural Influence Diffusion Model for Social Recommendation	Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang	In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation.
25	Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation	Thanh Tran, Renee Sweeney, Kyumin Lee	In this paper, we aim to solve the automatic playlist continuation (APC) problem by modeling complex interactions among users, playlists, and songs using only their interaction data.
26	Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation	Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, Xiaoli Li	This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task.
27	Transparent, Scrutable and Explainable User Models for Personalized Recommendation	Krisztian Balog, Filip Radlinski, Shushan Arakelyan	In this paper, we present a new set-based recommendation technique that permits the user model to be explicitly presented to users in natural language, empowering users to understand recommendations made and improve the recommendations dynamically.
28	A Capsule Network for Recommendation and Explaining What You Like and Dislike	Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu	To this end, in this paper, we propose a capsule network based model for rating prediction with user reviews, named CARP.
29	Reinforcement Knowledge Graph Reasoning for Explainable Recommendation	Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang	Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we aim to conduct explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure.
30	The FacT: Taming Latent Factor Models for Explainability with Factorization Trees	Yiyi Tao, Yiling Jia, Nan Wang, Hongning Wang	In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors.
31	Privacy-aware Document Ranking with Neural Signals	Jinjin Shao, Shiyu Ji, Tao Yang	This paper analyzes the critical leakages in interaction-based neural ranking and studies countermeasures to mitigate such a leakage. It proposes a privacy-aware neural ranking scheme that integrates tree ensembles with kernel value obfuscation and a soft match map based on adaptively-clustered term closures.
32	Legal Intelligence for E-commerce: Multi-task Learning by Leveraging Multiview Dispute Representation	Xin Zhou, Yating Zhang, Xiaozhong Liu, Changlong Sun, Luo Si	This paper proposes a novel research task of Legal Dispute Judgment (LDJ) prediction for e-commerce transactions, which connects two yet isolated domains, e-commerce data mining and legal intelligence.
33	Hierarchical Matching Network for Crime Classification	Pengfei Wang, Yu Fan, Shuzi Niu, Ze Yang, Yongfeng Zhang, Jiafeng Guo	In this paper, we formalize crime classification problem into a matching task to address these issues.
34	Learning from Fact-checkers: Analysis and Generation of Fact-checking Language	Nguyen Vo, Kyumin Lee	In this paper, we introduce a novel application of text generation for combating fake news.
35	A Collaborative Session-based Recommendation Approach with Parallel Memory Modules	Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, Maarten de Rijke	We propose a Collaborative Session-based Recommendation Machine (CSRM), a novel hybrid framework to apply collaborative neighborhood information to session-based recommendations.
36	Similarity-Based Synthetic Document Representations for Meta-Feature Generation in Text Classification	Sergio Canuto, Thiago Salles, Thierson C. Rosa, Marcos A. Gon?alves	We propose new solutions that enhance and extend the already very successful application of meta-features to text classification.
37	Finding Camouflaged Needle in a Haystack?: Pornographic Products Detection via Berrypicking Tree Model	Guoxiu He, Yangyang Kang, Zhe Gao, Zhuoren Jiang, Changlong Sun, Xiaozhong Liu, Wei Lu, Qiong Zhang, Luo Si	In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections.
38	Why do Users Issue Good Queries?: Neural Correlates of Term Specificity	Lauri Kangassalo, Michiel Spap?, Giulio Jacucci, Tuukka Ruotsalo	We investigated the association between the specificity of terms occurring in documents and human brain activity measured via electroencephalography (EEG).
39	Context Attentive Document Ranking and Query Suggestion	Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang	We present a context-aware neural ranking model to exploit users’ on-task search activities and enhance retrieval performance.
40	Information Needs, Queries, and Query Performance Prediction	Oleg Zendel, Anna Shtok, Fiana Raiber, Oren Kurland, J. Shane Culpepper	We demonstrate the far-reaching implications of this reality using standard TREC-based evaluation of QPP methods: their relative prediction quality patterns vary with respect to the effectiveness of queries used to represent the information needs. Motivated by our findings, we revise the basic probabilistic formulation of the QPP task by accounting for the information need and its connection to the query.
41	Task Completion Detection: A Study in the Context of Intelligent Systems	Ryen W. White, Ahmed Hassan Awadallah, Robert Sim	In this paper, we present methods to automatically detect task completion.
42	Bridging Gaps: Predicting User and Task Characteristics from Partial User Information	Matthew Mitsui, Chirag Shah	We approach this as a structure learning problem on incomplete data, determining the extent to which incomplete data can be used to predict user and task characteristics from interactions.
43	Human Behavior Inspired Machine Reading Comprehension	Yukun Zheng, Jiaxin Mao, Yiqun Liu, Zixin Ye, Min Zhang, Shaoping Ma	In this paper, we conduct a lab study to investigate human’s reading behavior patterns during reading comprehension tasks, where 32 users are recruited to take 60 distinct tasks.
44	EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation	Jiayi Zhang, Chongyang Tao, Zhenjing Xu, Qiaojing Xie, Wei Chen, Rui Yan	In this paper, we propose ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario.
45	User Attention-guided Multimodal Dialog Systems	Chen Cui, Wenjie Wang, Xuemeng Song, Minlie Huang, Xin-Shun Xu, Liqiang Nie	Towards this end, in this work, we present a hierarchical User attention-guided Multimodal Dialog system, named UMD for short.
46	Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence	Yaoming Zhu, Juncheng Wan, Zhiming Zhou, Liheng Chen, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu	In this paper, we argue that such a problem of maximum likelihood estimation is intrinsic, which is generally irrevocable via changing network structures.
47	Video Dialog via Multi-Grained Convolutional Self-Attention Context Networks	Weike Jin, Zhou Zhao, Mao Gu, Jun Yu, Jun Xiao, Yueting Zhuang	In this paper, we propose a novel approach for video dialog called multi-grained convolutional self-attention context network, which combines video information with dialog history.
48	Asking Clarifying Questions in Open-Domain Information-Seeking Conversations	Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, W. Bruce Croft	In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems. To this end, we propose an offline evaluation methodology for the task and collect a dataset, called Qulac, through crowdsourcing.
49	Accelerated Query Processing Via Similarity Score Prediction	Matthias Petri, Alistair Moffat, Joel Mackenzie, J. Shane Culpepper, Daniel Beck	In this work, we consider algorithmic properties associated with dynamic pruning mechanisms.
50	Optimal Freshness Crawl Under Politeness Constraints	Andrey Kolobov, Yuval Peres, Eyal Lubetzky, Eric Horvitz	In this paper, we introduce PoliteBinaryLambdaCrawl, the first optimal algorithm for freshness crawl scheduling in the presence of politeness constraints as well as non-uniform page importance scores and the crawler’s own crawl request limit.
51	Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors	Juli?n Urbano, Harlley Lima, Alan Hanjalic	In contrast to past studies, in this paper we employ a recent simulation methodology from TREC data to go around these limitations.
52	CROSS: Cross-platform Recommendation for Social E-Commerce	Tzu-Heng Lin, Chen Gao, Yong Li	In this paper, we address the problem of cross-platform recommendation for social e-commerce, i.e., recommending products to users when they are shopping through social media.
53	Hot Topic-Aware Retweet Prediction with Masked Self-attentive Model	Renfeng Ma, Xiangkun Hu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang	To make efficient use of hot topics, we propose a novel masked self-attentive model to perform the retweet prediction task by perceiving the hot topics discussed by the users’ followees.
54	Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning	Tao Gui, Peng Liu, Qi Zhang, Liang Zhu, Minlong Peng, Yunhua Zhou, Xuanjing Huang	In this work, we propose the use of a novel cooperative multi-agent approach to mention recommendation, which incorporates dozens of more historical tweets than earlier approaches.
55	Online User Representation Learning Across Heterogeneous Social Networks	Weiqing Wang, Hongzhi Yin, Xingzhong Du, Wen Hua, Yongjun Li, Quoc Viet Hung Nguyen	In this paper, we propose MV-URL, a multi-view user representation learning model to enhance user modeling by integrating the knowledge from various networks.
56	PSGAN: A Minimax Game for Personalized Search with Limited and Noisy Click Data	Shuqi Lu, Zhicheng Dou, Xu Jun, Jian-Yun Nie, Ji-Rong Wen	In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search.
57	Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction	Kan Ren, Jiarui Qin, Yuchen Fang, Weinan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai	In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
58	Multi-view Embedding-based Synonyms for Email Search	Cheng Li, Mingyang Zhang, Michael Bendersky, Hongbo Deng, Donald Metzler, Marc Najork	Therefore, in this paper, we propose a solution tailored to the challenges of synonym expansion for email search.
59	Context-Aware Intent Identification in Email Conversations	Wei Wang, Saghar Hosseini, Ahmed Hassan Awadallah, Paul N. Bennett, Chris Quirk	In this paper, we study intent identification in workplace email.
60	Which Diversity Evaluation Measures Are “Good”?	Tetsuya Sakai, Zhaohao Zeng	This study evaluates 30 IR evaluation measures or their instances, of which nine are for adhoc IR and 21 are for diversified IR, primarily from the viewpoint of whether their preferences of one SERP (search engine result page) over another actually align with users’ preferences.
61	Investigating Passage-level Relevance and Its Role in Document-level Relevance Judgment	Zhijing Wu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma	A thorough analysis reveals that: 1) there is a strong correlation between the document-level relevance and the fractions of irrelevant passages to highly relevant passages; 2) the position, length and query similarity of passages play different roles in the determination of document-level relevance; 3) The sequential passage-level relevance within a document is a potential indicator for the document-level relevance.
62	Jointly Modeling Relevance and Sensitivity for Search Among Sensitive Content	Mahmoud F. Sayed, Douglas W. Oard	This paper proposes an approach that leverages learning to rank techniques.
63	Revisiting Online Personal Search Metrics with the User in Mind	Azin Ashkan, Donald Metzler	The proposed framework introduces a variant of online metrics called pMetrics (short for personalized metrics) that are based on the average search habits of users for the relevance signal of interest.
64	Scalable Deep Multimodal Learning for Cross-Modal Retrieval	Peng Hu, Liangli Zhen, Dezhong Peng, Pei Liu	In this paper, we present a novel cross-modal retrieval method, called Scalable Deep Multimodal Learning (SDML).
65	Neural-Network Lexical Translation for Cross-lingual IR from Text and Speech	Rabih Zbib, Lingjun Zhao, Damianos Karakos, William Hartmann, Jay DeYoung, Zhongqiang Huang, Zhuolin Jiang, Noah Rivkin, Le Zhang, Richard Schwartz, John Makhoul	We propose a neural network model to estimate word translation probabilities for Cross-Lingual Information Retrieval (CLIR).
66	Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos	Zhu Zhang, Zhijie Lin, Zhou Zhao, Zhenxin Xiao	In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including (1) the syntactic structure of natural language queries; (2) long-range semantic dependencies in video context and (3) the sufficient cross-modal interaction.
67	Bayesian Personalized Feature Interaction Selection for Factorization Machines	Yifan Chen, Pengjie Ren, Yang Wang, Maarten de Rijke	In this work, we address this issue and study Personalized Feature Interaction Selection (P-FIS) by proposing a Bayesian Personalized Feature Interaction Selection (BP-FIS) mechanism under the Bayesian Variable Selection (BVS) theory.
68	CTRec: A Long-Short Demands Evolution Model for Continuous-Time Recommendation	Ting Bai, Lixin Zou, Wayne Xin Zhao, Pan Du, Weidong Liu, Jian-Yun Nie, Ji-Rong Wen	In this paper, we propose a novel self-attentive Continuous-Time Recommendation model (CTRec) for capturing the evolving demands of users over time.
69	p-Net: A Parallel Information-sharing Network for Shared-account Cross-domain Sequential Recommendations	Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Jun Ma, Maarten de Rijke	We propose a Parallel Information-sharing Network (p-Net) to simultaneously generate recommendations for two domains where user behaviors on two domains are synchronously shared at each timestamp.
70	Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings	Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He	In this paper, we aim to improve CTR predictions during both the cold-start phase and the warm-up phase when a new ad is added to the candidate pool.
71	Effects of User Negative Experience in Mobile News Streaming	Hongyu Lu, Min Zhang, Weizhi Ma, Ce Wang, Feng xia, Yiqun Liu, Leyu Lin, Shaoping Ma	In this work, a retrospective analysis is conducted using real users’ log data, containing user’s explicit feedback of negative experiences, from a commercial news streaming application.
72	Online Multi-modal Hashing with Dynamic Query-adaption	Xu Lu, Lei Zhu, Zhiyong Cheng, Liqiang Nie, Huaxiang Zhang	To address the above limitations, in this paper, we propose an Online Multi-modal Hashing with Dynamic Query-adaption (OMH-DQ) method in a novel fashion.
73	Supervised Hierarchical Cross-Modal Hashing	Changchang Sun, Xuemeng Song, Fuli Feng, Wayne Xin Zhao, Hao Zhang, Liqiang Nie	In this paper, we propose a new end-to-end solution for supervised cross-modal hashing, named HiCHNet, which explicitly exploits the hierarchical labels of instances. Due to the lack of benchmark datasets, apart from adapting the existing dataset FashionVC from fashion domain, we create a dataset from the online fashion platform Ssense consisting of 15,696 image-text pairs labeled by 32 hierarchical categories.
74	Unsupervised Neural Generative Semantic Hashing	Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma	We present a novel unsupervised generative semantic hashing approach, Ranking based Semantic Hashing (RBSH) that consists of both a variational and a ranking based component.
75	Outline Generation: Understanding the Inherent Content Structure of Documents	Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng	In this paper, we introduce and tackle the Outline Generation (OG) task, which aims to unveil the inherent content structure of a multi-paragraph document by identifying its potential sections and generating the corresponding section headings. Besides, we build a novel Wriptsize IKI OG dataset, a public collection which consists of over 1.75 million document-outline pairs for research on the OG task.
76	DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases	Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng, Jian-Yun Nie	This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document.
77	Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation	Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, Hongyuan Zha	This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information.
78	Interpretable Fashion Matching with Rich Attributes	Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat-Seng Chua	We propose a new solution named A ttribute-based I nterpretable C ompatibility (AIC) method, which consists of three modules: 1) a tree-based module that extracts decision rules on matching prediction; 2) an embedding module that learns vector representation for a rule by accounting for the attribute semantics; and 3) a joint modeling module that unifies the visual embedding and rule embedding to predict the matching score.
79	Prototype-guided Attribute-wise Interpretable Scheme for Clothing Matching	Xianjing Han, Xuemeng Song, Jianhua Yin, Yinglong Wang, Liqiang Nie	Considering that the research line of the comprehensively interpretable clothing matching is largely untapped, in this work, we propose a prototype-guided attribute-wise interpretable compatibility modeling (PAICM) scheme, which seamlessly integrates the latent compatible/incompatible prototype learning and compatibility modeling with the Bayesian personalized ranking (BPR) framework.
80	Teach Machine How to Read: Reading Behavior Inspired Relevance Estimation	Xiangsheng Li, Jiaxin Mao, Chao Wang, Yiqun Liu, Min Zhang, Shaoping Ma	In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment.
81	Improving the Accuracy of System Performance Estimation by Using Shards	Nicola Ferro, Mark Sanderson	We improve the measurement accuracy of retrieval system performance by better modeling the noise present in test collection scores.
82	Fast Approximate Filtering of Search Results Sorted by Attribute	Franco Maria Nardini, Roberto Trani, Rossano Venturini	In this paper, we propose epsilon-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list.
83	Intervention Harvesting for Context-Dependent Examination-Bias Estimation	Zhichong Fang, Aman Agarwal, Thorsten Joachims	To overcome this limitation, we propose a Contextual Position-Based Model (CPBM) where the examination bias may also depend on a context vector describing the query and the user.
84	Variance Reduction in Gradient Exploration for Online Learning to Rank	Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, Hongning Wang	In this work, we aim at reducing the variance of gradient estimation in OL2R algorithms.
85	Yelling at Your TV: An Analysis of Speech Recognition Errors and Subsequent User Behavior on Entertainment Systems	Raphael Tang, Ferhan Ture, Jimmy Lin	We provide both quantitative and qualitative analyses, examining the acoustic as well as lexical attributes of the utterances.
86	Learning More From Less: Towards Strengthening Weak Supervision for Ad-Hoc Retrieval	Dany Haddad, Joydeep Ghosh	Building on these insights, we propose two methods to reduce the amount of training data required.
87	Network Embedding and Change Modeling in Dynamic Heterogeneous Networks	Ranran Bian, Yun Sing Koh, Gillian Dobbie, Anna Divoli	We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps.
88	From Text to Sound: A Preliminary Study on Retrieving Sound Effects to Radio Stories	Songwei Ge, Curtis Xuan, Ruihua Song, Chao Zou, Wei Liu, Jin Zhou	In this paper, we address the problem of automatically adding sound effects to radio stories with a retrieval-based model.
89	Length-adaptive Neural Network for Answer Selection	Taihua Shao, Fei Cai, Honghui Chen, Maarten de Rijke	To address this issue, we propose a Length-adaptive Neural Network (LaNN) for answer selection that can auto-select a neural feature extractor according to the length of the input sentence.
90	Embedding Edge-attributed Relational Hierarchies	Muhao Chen, Chris Quirk	In this paper, we propose a novel embedding method that simultaneously preserve the hierarchical property and the edge information in the edge-attributed relational hierarchies.
91	Leveraging Emotional Signals for Credibility Detection	Anastasia Giachanou, Paolo Rosso, Fabio Crestani	In this paper, we study the role of emotional signals in fake news detection.
92	Encoding Syntactic Dependency and Topical Information for Social Emotion Classification	Chang Wang, Bang Wang, Wei Xiang, Minghua Xu	In this paper, we design a new neural network model by encoding sentence syntactic dependency and document topical information into the document representation.
93	Information Cascades Modeling via Deep Multi-Task Learning	Xueqin Chen, Kunpeng Zhang, Fan Zhou, Goce Trajcevski, Ting Zhong, Fengli Zhang	In this paper, we propose a deep multi-task learning framework with a novel design of shared-representation layer to aid in explicitly understanding and predicting the cascades.
94	An Analysis of the Change in Discussions on Social Media with Bitcoin Price	Andrew Burnie, Emine Yilmaz	We develop a new approach to temporalizing word2vec-based topic modelling that determines which topics on social media vary with shifts in the phases of a time series to understand potential interactions.
95	Ensembles of Recurrent Networks for Classifying the Relationship of Fake News Titles	Ting Su, Craig Macdonald, Iadh Ounis	In this paper, we investigate the advantages of recurrent neural networks-based language representations (e.g., BERT, BiLSTM) in order to build ensemble classifiers that can accurately predict if one news title is related to, and, additionally disagrees with an earlier news title.
96	Contextually Propagated Term Weights for Document Representation	Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma	We present a novel model that, given a target word, redistributes part of that word’s weight (that has been computed with word embeddings) across words occurring in similar contexts as the target word.
97	Time-Limits and Summaries for Faster Relevance Assessing	Shahin Rahbariasl, Mark D. Smucker	We conducted a user study with 60 participants where we investigated the impact of time limits (15, 30, and 60 seconds) and document size (full length vs. short summaries) on relevance assessing.
98	Deep Collaborative Discrete Hashing with Semantic-Invariant Structure	Zijian Wang, Zheng Zhang, Yadan Luo, Zi Huang	This paper proposes a dual-stream learning framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantic labels.
99	On Topic Difficulty in IR Evaluation: The Effect of Systems, Corpora, and System Components	Fabio Zampieri, Kevin Roitero, J. Shane Culpepper, Oren Kurland, Stefano Mizzaro	In this paper we study the effects on the topic difficulty of: (i) the set of retrieval systems; (ii) the underlying document corpus; and (iii) the system components.
100	M-HIN: Complex Embeddings for Heterogeneous Information Networks via Metagraphs	Yang Fang, Xiang Zhao, Peixin Huang, Weidong Xiao, Maarten de Rijke	Inspired by developments in knowledge graph embedding, we propose to construct HIN triplets using nodes and metagraphs between them.
101	How to Deal with Scarce Annotations in Answer Selection	Emmanuel Vallee, Delphine Charlet, Gabriel Marzinotto, Fabrice Clerot, Frank Meyer	In this work, we are interested in simple models that can potentially give good performance on datasets with no or few annotations.
102	One-Class Collaborative Filtering with the Queryable Variational Autoencoder	Ga Wu, Mohamed Reda Bouadjenek, Scott Sanner	To address this issue, we propose a novel Queryable Variational Autoencoder (Q-VAE) variant of the VAE that explicitly models arbitrary conditional relationships between observations.
103	Developing Evaluation Metrics for Instant Search Using Mixed Methods Methods	Praveen Chandar, Jean Garcia-Gathright, Christine Hosey, Brian St. Thomas, Jennifer Thom	In this work, we describe a mixed methods approach to understanding user expectations and evaluating an instant search system in the context of music search.
104	FAQ Retrieval Using Attentive Matching	Sparsh Gupta, Vitor R. Carvalho	In this paper we propose multiple deep learning architectures designed for FAQ Retrieval that eliminate the need for feature engineering and are able to elegantly combine both query-question and query-answer similarities.
105	Item Recommendation by Combining Relative and Absolute Feedback Data	Saikishore Kalloori, Tianyu Li, Francesco Ricci	Hence, in this work, we develop a ranking technique that separately exploits absolute and relative preferences in a hybrid model.
106	Multiple Query Processing via Logic Function Factoring	Matteo Catena, Nicola Tonellotto	In this work, we propose an approach to optimize the processing of query variations to reduce their overall response time.
107	Information Nutritional Label and Word Embedding to Estimate Information Check-Worthiness	C?dric Lespagnol, Josiane Mothe, Md Zia Ullah	In this paper, we revisit the information check-worthiness problem and propose a method that combines the “information nutritional label” features with POS-tags and word-embedding representations.
108	Unbiased Low-Variance Estimators for Precision and Related Information Retrieval Effectiveness Measures	Gordon V. Cormack, Maura R. Grossman	This work describes an estimator from which unbiased measurements of precision, rank-biased precision, and cumulative gain may be derived from a uniform or non-uniform sample of relevance assessments.
109	Multi-Level Matching Networks for Text Matching	Chunlin Xu, Zhiwei Lin, Shengli Wu, Hui Wang	Therefore, instead of making decisions utilizing single level word representations, a multi-level matching network (MMN) is proposed in this paper for text matching, which utilizes multiple levels of word representations to obtain multiple word level matching results for final text level matching decision.
110	Investigating the Interplay Between Searchers’ Privacy Concerns and Their Search Behavior	Steven Zimmerman, Alistair Thorpe, Chris Fox, Udo Kruschwitz	Our primary goal is to understand the extent to which attitudes towards privacy are linked to behaviors that protect privacy.
111	A Systematic Comparison of Methods for Finding Good Premises for Claims	Lorik Dumani, Ralf Schenkel	In this paper we compare 196 methods systematically for determining similar claims by textual similarity, using a large corpus of (claim, premise) pairs crawled from debate portals.
112	Graph Intention Network for Click-through Rate Prediction in Sponsored Search	Feng Li, Zhenrui Chen, Pengjie Wang, Yi Ren, Di Zhang, Xiaoyu Zhu	We propose a new approach Graph Intention Network (GIN) based on co-occurrence commodity graph to mine user intention.
113	A Scalable Virtual Document-Based Keyword Search System for RDF Datasets	Dennis Dosso, Gianmaria Silvello	This work describes the TSA+VDP keyword search system for effective and efficient keyword search over large RDF datasets.
114	Query-Task Mapping	Michael V?lske, Ehsan Fatehifar, Benno Stein, Matthias Hagen	Several recent task-based search studies aim at splitting query logs into sets of queries for the same task or information need. We address the natural next step: mapping a currently submitted query to an appropriate task in an already task-split log.
115	An Analysis of Query Reformulation Techniques for Precision Medicine	Maristella Agosti, Giorgio Maria Di Nunzio, Stefano Marchesin	In the paper, we take advantage of this opportunity and we propose and evaluate state-of-the-art query expansion and reduction techniques to identify whether a particular approach can be helpful in both scientific literature and clinical trial retrieval.
116	Using Trails to Support Users with Tasks of Varying Scope	Robert Capra, Jaime Arguello	In this paper, we examine two questions. First, what are task characteristics that influence a user’s ability to gain benefits from others’ trails? Second, what is the impact of a “mismatch” between a current user’s task and previous user’s task which originated the trail?
117	An Axiomatic Approach to Regularizing Neural Ranking Models	Corby Rosset, Bhaskar Mitra, Chenyan Xiong, Nick Craswell, Xia Song, Saurabh Tiwary	This work explores the use of IR axioms to augment the direct supervision from labeled data for training neural ranking models.
118	Deeper Text Understanding for IR with Contextual Neural Language Modeling	Zhuyun Dai, Jamie Callan	This paper proposes a Deep Contextualized Term Weighting framework (DeepCT) that identifies import terms by taking into consideration the meaning of the term and the role it plays in a specific context.
119	Understanding the Interpretability of Search Result Summaries	Siyu Mi, Jiepu Jiang	We examine the interpretability of search results in current web search engines through a lab user study.
120	Content-Based Weak Supervision for Ad-Hoc Re-Ranking	Sean MacAvaney, Andrew Yates, Kai Hui, Ophir Frieder	In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs).
121	Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search	Pepa Atanasova, Georgi Karadzhov, Yasen Kiprov, Preslav Nakov, Fabrizio Sebastiani	How to produce good variable-length lists, given the conflicting objectives of staying short while maximizing the likelihood of having a correct answer included in the list, is an underexplored problem. It is also unclear how to evaluate a system that tries to do that. Here we aim to bridge this gap.
122	On Tradeoffs Between Document Signature Methods for a Legal Due Diligence Corpus	Adam Roegiest, Edward Lee	We present an examination of the tradeoffs that document signature methods face in the due diligence domain.
123	A study on the Interpretability of Neural Retrieval Models using DeepSHAP	Zeon Trevor Fernando, Jaspreet Singh, Avishek Anand	In this paper we explored various reference input document construction techniques.
124	Automatic Task Completion Flows from Web APIs	Kyle Williams, Seyyed Hadi Hashemi, Imed Zitouni	We propose a method to automatically produce task completion flows from a collection of these APIs by combining them in a graph and automatically extracting paths from the graph for task completion.
125	Ontology-Aware Clinical Abstractive Summarization	Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, Ross W. Filice	We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation.
126	Hyperlink Classification via Structured Graph Embedding	Geon Lee, Seonggoo Kang, Joyce Jiyoung Whang	We formally define a hyperlink classification problem in web search by classifying hyperlinks into three classes based on their roles: navigation, suggestion, and action.
127	Answer-enhanced Path-aware Relation Detection over Knowledge Base	Daoyuan Chen, Min Yang, Hai-Tao Zheng, Yaliang Li, Ying Shen	In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection.
128	Block-distributed Gradient Boosted Trees	Theodore Vasiloudis, Hyunsu Cho, Henrik Bostr?m	In order to allow for scalability across both the data point and feature dimensions, and reduce communication cost, we propose block-distributed GBTs.
129	Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval	Li Zhang, Shuo Zhang, Krisztian Balog	We employ neural language modeling approaches to embed tabular data into vector spaces.
130	Coarse-to-Fine Grained Classification	Yuqi Huo, Yao Lu, Yulei Niu, Zhiwu Lu, Ji-Rong Wen	In this paper, we define a new problem called coarse-to-fine grained classification (C2FGC) which aims to recognize the classes of objects in multiple resolutions (from low to high).
131	Local Matrix Approximation based on Graph Random Walk	Xuejiao Yang, Bang Wang	In this paper, we propose a novel scheme for local matrix construction without involving distance computation.
132	A Context-based Framework for Resource Citation Classification in Scientific Literatures	He Zhao, Zhunchen Luo, Chong Feng, Yuming Ye	In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations.
133	Text Retrieval Priors for Bayesian Logistic Regression	Eugene Yang, David D. Lewis, Ophir Frieder	Building on past work, we show that BM25-inspired Gaussian priors for Bayesian logistic regression based on topical keywords provide better effectiveness than the usual L2 (zero mode, uniform variance) Gaussian prior.
134	A Lightweight Representation of News Events on Social Media	Mauricio Quezada, Barbara Poblete	We propose a lightweight representation of newsworthy social media data.
135	Analyzing and Predicting News Popularity in an Instant Messaging Service	Mohammad Naseri, Hamed Zamani	In this paper, we provide an initial study to analyze and predict news popularity in an instant messaging service.
136	Adversarial Training for Review-Based Recommendations	Dimitrios Rafailidis, Fabio Crestani	In this paper, we propose an Adversarial Training approach for Review-based recommendations, namely ATR.
137	A New Perspective on Score Standardization	Juli?n Urbano, Harlley Lima, Alan Hanjalic	In this paper, we show that both approaches are actually special cases of a simple standardization which assumes specific distributions for the per-topic scores.
138	Adversarial Collaborative Neural Network for Robust Recommendation	Feng Yuan, Lina Yao, Boualem Benatallah	In this work, we propose a general adversarial training framework for NN-based recommendation models, improving both the model robustness and the overall performance.
139	Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN	Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff	In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case.
140	Addressing Vocabulary Gap in E-commerce Search	Subhadeep Maji, Rohan Kumar, Manish Bansal, Kalyani Roy, Mohit Kumar, Pawan Goyal	Addressing Vocabulary Gap in E-commerce Search
141	Gated Spectral Units: Modeling Co-evolving Patterns for Sequential Recommendation	Lei Zheng, Ziwei Fan, Chun-Ta Lu, Jiawei Zhang, Philip S. Yu	In order to capture the co-evolving knowledge for sequential recommendations, we start from introducing an efficient spectral convolution operation to discover complex relationships between users and items from the spectral domain of a graph, where the hidden connectivity information of the graph can be revealed.
142	Deep Distribution Network: Addressing the Data Sparsity Issue for Top-N Recommendation	Lei Zheng, Chaozhuo Li, Chun-Ta Lu, Jiawei Zhang, Philip S. Yu	In this work, in order to overcome the two aforementioned drawbacks, we propose Deep Distribution Network (DDN) to model users and items via Gaussian distributions.
143	Normalized Query Commitment Revisited	Haggai Roitman	We revisit the Normalized Query Commitment (NQC) query performance prediction (QPP) method.
144	Quantifying Bias and Variance of System Rankings	Gordon V. Cormack, Maura R. Grossman	We derive from tau a distance between rankings in Euclidean space, from which we can determine the magnitude of bias, variance, and error.
145	Towards Better Support for Exploratory Search through an Investigation of Notes-to-self and Notes-to-share	Anita Crescenzi, Yuan Li, Yinglong Zhang, Rob Capra	In this work, we investigated how people organize and structure information they discover during exploratory searches.
146	The Emotion Profile of Web Search	Gabriella Kazai, Paul Thomas, Nick Craswell	In this paper, we study the emotion profile of retrieved and clicked web search results towards the goal of better understanding the role of emotions in web search.
147	CEDR: Contextualized Embeddings for Document Ranking	Sean MacAvaney, Andrew Yates, Arman Cohan, Nazli Goharian	In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking.
148	Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis	Hao-Ming Fu, Pu-Jen Cheng	In this paper, we propose a model that overcomes difficulties encountered by both families of methods.
149	Evaluating Resource-Lean Cross-Lingual Embedding Models in Unsupervised Retrieval	Robert Litschko, Goran Glava?, Ivan Vulic, Laura Dietz	In this work, we provide a comprehensive comparative evaluation of projection-based CLE models for both sentence-level and document-level cross-lingual Information Retrieval (CLIR).
150	FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance	Wataru Sakata, Tomohide Shibata, Ribeka Tanaka, Sadao Kurohashi	We propose a FAQ retrieval system that considers the similarity between a user’s query and a question as well as the relevance between the query and an answer.
151	Argument Search: Assessing Argument Relevance	Martin Potthast, Lukas Gienapp, Florian Euchner, Nick Heilenk?tter, Nico Weidmann, Henning Wachsmuth, Benno Stein, Matthias Hagen	We report on the first user study on assessing argument relevance.
152	Order-aware Embedding Neural Network for CTR Prediction	Wei Guo, Ruiming Tang, Huifeng Guo, Jianhua Han, Wen Yang, Yuzhou Zhang	To solve these problems, we propose a novel technique named Order-aware Embedding (i.e., multi-embeddings are learned for each feature, and different embeddings are applied for feature interactions of different orders), which can be applied to various models and generates feature interactions more effectively.
153	The Impact of Score Ties on Repeatability in Document Ranking	Jimmy Lin, Peilin Yang	This short paper examines the effectiveness impact of such score ties, quantifying the variability that can be attributed to this phenomenon.
154	Critically Examining the “Neural Hype”: Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models	Wei Yang, Kuang Lu, Peilin Yang, Jimmy Lin	This paper provides a rigorous evaluation of those claims in two ways: First, we conducted a meta-analysis of papers that have reported experimental results on the TREC Robust04 test collection.
155	BERT with History Answer Embedding for Conversational Question Answering	Chen Qu, Liu Yang, Minghui Qiu, W. Bruce Croft, Yongfeng Zhang, Mohit Iyyer	We propose a conceptually simple yet highly effective approach referred to as history answer embedding.
156	On the Effect of Low-Frequency Terms on Neural-IR Models	Sebastian Hofst?tter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury	In this paper, we analyze the effects of low-frequency terms on the performance and robustness of neural IR models.
157	Effective Medical Archives Processing Using Knowledge Graphs	Xiaoli Wang, Rongzhen Wang, Zhifeng Bao, Jiayin Liang, Wei Lu	In this paper, we focus on empowering the medical archives processing with knowledge graphs.
158	Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network	Chen Zhang, Qiuchi Li, Dawei Song	In this paper, we propose a proximity-weighted convolution network to offer an aspect-specific syntax-aware representation of contexts.
159	A Study on Agreement in PICO Span Annotations	Grace E. Lee, Aixin Sun	In this paper, we study the agreement of PICO annotations made by multiple human annotators, including both experts and non-experts.
160	Corpus-based Set Expansion with Lexical Features and Distributed Representations	Puxuan Yu, Zhiqi Huang, Razieh Rahimi, James Allan	We present CaSE, an efficient unsupervised corpus-based set expansion framework that leverages lexical features as well as distributed representations of entities for the set expansion task.
161	Sparse Tensor Co-clustering as a Tool for Document Categorization	Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif	In this paper, we extend the use of the Sparse Poisson Latent Block Model to deal with sparse tensor data using jointly all information arising from documents.
162	Numeral Attachment with Auxiliary Tasks	Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen	In this paper we propose the task of numeral attachment to detect the attached target of a numeral.
163	Decoding The Style And Bias of Song Lyrics	Manash Pratim Barman, Amit Awekar, Sambhav Kothari	The central idea of this paper is to gain a deeper understanding of song lyrics computationally.
164	Retrieving Multi-Entity Associations: An Evaluation of Combination Modes for Word Embeddings	Gloria Feher, Andreas Spitz, Michael Gertz	In this paper, we use popular embedding methods to train vector representations of an entity-annotated news corpus, and evaluate their performance for the task of predicting entity participation in news events versus a traditional word cooccurrence network as a baseline.
165	Modeling Transferable Topics for Cross-Target Stance Detection	Penghui Wei, Wenji Mao	In this paper we study the problem of cross-target stance detection, utilizing labeled data of a source target to learn models that can be adapted to a destination target.
166	TDP: Personalized Taxi Demand Prediction Based on Heterogeneous Graph Embedding	Zhenlong Zhu, Ruixuan Li, Minghui Shan, Yuhua Li, Lu Gao, Fei Wang, Jixing Xu, Xiwu Gu	To address these challenges, in this paper, we propose Taxi Demand Prediction (TDP) model in challenging entertainment scene based on heterogeneous graph embedding and deep neural predicting network.
167	Uncovering Insurance Fraud Conspiracy with Network Learning	Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, Yuan Qi	In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters.
168	Training Streaming Factorization Machines with Alternating Least Squares	Xueyu Mao, Saayan Mitra, Sheng Li	In this paper, we propose an online training scheme for FM with the alternating least squares (ALS) technique, which has comparable performance with existing batch training algorithms.
169	Social Attentive Deep Q-network for Recommendation	Yu Lei, Zhitao Wang, Wenjie Li, Hongbin Pei	In this work, we develop a Social Attentive Deep Q-network (SADQN) agent, which is able to provide high-quality recommendations during user-agent interactions by leveraging social influence among users.
170	Cleaned Similarity for Better Memory-Based Recommenders	Farhan Khawar, Nevin L. Zhang	In this paper, we analyze the spectral properties of the Pearson and the cosine similarity estimators, and we use tools from random matrix theory to argue that they suffer from noise and eigenvalues spreading.
171	ABCPRec: Adaptively Bridging Consumer and Producer Roles for User-Generated Content Recommendation	Kosetsu Tsukuda, Satoru Fukayama, Masataka Goto	In this paper, based on the state-of-the-art UGC recommendation method called CPRec (consumer and producer based recommendation), we propose ABCPRec (adaptively bridging CPRec).
172	Improving Collaborative Metric Learning with Efficient Negative Sampling	Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier, Manuel Moussallam	To alleviate this problem, we propose here a 2-stage negative sampling strategy which finds triplets that are highly informative for learning.
173	SAIN: Self-Attentive Integration Network for Recommendation	Seongjun Yun, Raehyun Kim, Miyoung Ko, Jaewoo Kang	In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task.
174	Towards Context-Aware Evaluation for Image Search	Yunqiu Shao, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma	In this paper, we pay attention to the context factor in the image search scenario.
175	A Horizontal Patent Test Collection	Mihai Lupu, Alexandros Bampoulidis, Luca Papariello	We motivate the need for, and describe the contents of a novel patent research collection, publicly available and for free, covering multimodal and multilingual data from six patent authorities.
176	Dynamic Sampling Meets Pooling	Gordon V. Cormack, Haotian Zhang, Nimesh Ghelani, Mustafa Abualsaud, Mark D. Smucker, Maura R. Grossman, Shahin Rahbariasl, Amira Ghenai	The results suggest that the use of Dynamic Sampling without pooling can, for an order of magnitude less assessment effort, yield information-retrieval effectiveness estimates that exhibit lower bias, lower error, and comparable ability to rank system effectiveness.
177	Help Me Search: Leveraging User-System Collaboration for Query Construction to Improve Accuracy for Difficult Queries	Saar Kuzi, Abhishek Narwekar, Anusri Pampari, ChengXiang Zhai	In this paper, we address the problem of difficult queries by using a novel strategy of collaborative query construction where the search engine would actively engage users in an iterative process to continuously revise a query.
178	On Anonymous Commenting: A Greedy Approach to Balance Utilization and Anonymity for Instagram Users	Arian Askari, Asal Jalilvand, Mahmood Neshati	In this paper, we explore anonymous commenting approaches and their pros and cons.
179	Neural Compatibility Ranking for Text-based Fashion Matching	SUTHEE CHAIDAROON, Yi Fang, Min Xie, Alessandro Magnani	To address the task of fashion matching, we propose a neural compatibility model for ranking fashion products based on the compatibility matching with the input outfit.
180	NRPA: Neural Recommendation with Personalized Attention	Hongtao Liu, Fangzhao Wu, Wenjun Wang, Xianchen Wang, Pengfei Jiao, Chuhan Wu, Xing Xie	In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.
181	Vertical Search Blending: A Real-world Counterfactual Dataset	Pavel Proch?zka, Matej Koci?n, Jakub Drd?k, Jan Vr?ovsk?, Vladim?r Kadlec, Jaroslav Kuchar	We release a large-scale, real-world vertical-blending dataset gathered bySeznam.cz web search engine.
182	Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks	Sebastian Bruch, Masrour Zoghi, Michael Bendersky, Marc Najork	Through this study, we hope to show that the ideas from that work are more relevant than ever and can lay the foundation of learning-to-rank research in the age of deep neural networks.
183	Name Entity Recognition with Policy-Value Networks	Yadi Lao, Jun Xu, Sheng Gao, Jun Guo, Ji-Rong Wen	In this paper we propose a novel reinforcement learning based model for named entity recognition (NER), referred to as MM-NER.
184	Revealing the Role of User Moods in Struggling Search Tasks	Luyan Xu, Xuan Zhou, Ujwal Gadiraju	In this work, we show that a user’s own mood. can systematically bias the user’s perception, and experience while interacting with a search system and trying to satisfy an information need.
185	Selecting Discriminative Terms for Relevance Model	Dwaipayan Roy, Sumit Bhatia, Mandar Mitra	We propose three possible extensions of the relevance model that address this drawback.
186	A Dataset of Systematic Review Updates	Amal Alharbi, Mark Stevenson	This paper describes a dataset of systematic review updates in the field of medicine created using 25 Cochrane reviews.
187	Query Performance Prediction for Pseudo-Feedback-Based Retrieval	Haggai Roitman, Oren Kurland	We address the prediction challenge for pseudo-feedback-based retrieval methods which utilize an initial retrieval to induce a new query model; the query model is then used for a second (final) retrieval.
188	Reinforcement Learning for User Intent Prediction in Customer Service Bots	Cen Chen, Chilin Fu, Xu Hu, Xiaolu Zhang, Jun Zhou, Xiaolong Li, Forrest Sheng Bao	Hence, we propose to view the problem as a sequential decision making process to better capture the long-term effects of each recommendation in the list.
189	Enhanced News Retrieval: Passages Lead the Way!	Matteo Catena, Ophir Frieder, Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto	Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of news articles.
190	Contextual Dialogue Act Classification for Open-Domain Conversational Agents	Ali Ahmadvand, Jason Ingyu Choi, Eugene Agichtein	To address these problems, we propose a novel method, CDAC (Contextual Dialogue Act Classifier), a simple yet effective deep learning approach for contextual dialogue act classification.
191	Accelerating Exact Inner Product Retrieval by CPU-GPU Systems	Long Xiang, Bo Tang, Chuan Yang	In this work, we analyze the time cost of each phase in IPR solutions at first. Second, we exploit the characteristics of CPU-GPU systems to improve performance.
192	LIRME: Locally Interpretable Ranking Model Explanation	Manisha Verma, Debasis Ganguly	However, there is no systematic investigation that learns to interpret IR models, which is in fact the core contribution of our work in this paper. We explore three sampling methods to train an explanation model and propose two metrics to evaluate explanations generated for an IR model.
193	Solr Integration in the Anserini Information Retrieval Toolkit	Ryan Clancy, Toke Eskildsen, Nick Ruest, Jimmy Lin	To illustrate the additional capabilities enabled by Anserini/Solr integration, we present a search interface built using the open-source Blacklight discovery interface.
194	Parrot: A Python-based Interactive Platform for Information Retrieval Research	Xinhui Tu, Jimmy Huang, Jing Luo, Runjie Zhu, Tingting He	In this paper, we propose Parrot1, a Python-based interactive platform for information retrieval research.
195	An Open-Access Platform for Transparent and Reproducible Structured Searching	Tony Russell-Rose, Jon Chamberlain	In this paper, we demonstrate a new approach to structured searching in which concepts are expressed as objects on a two-dimensional canvas.
196	MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching	Jiafeng Guo, Yixing Fan, Xiang Ji, Xueqi Cheng	In this paper, therefore, we present a novel system, namely MatchZoo, to facilitate the learning, practicing and designing of neural text matching models.
197	AgentBuddy: an IR System based on Bandit Algorithms to Reduce Cognitive Load for Customer Care Agents	Hrishikesh Ganu, Mithun Ghosh, Freddy Jose, Shashi Roshan	We describe a human-in-the loop system – AgentBuddy, that is helping Intuit improve the quality of search it offers to its internal Customer Care Agents (CCAs).
198	AliISA: Creating an Interactive Search Experience in E-commerce Platforms	Fei Xiao, Zhen Wang, Haikuan Huang, Jun Huang, Xi Chen, Hongbo Deng, Minghui Qiu, Xiaoli Gong	In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.
199	SCSS-LIE: A Novel Synchronous Collaborative Search System with a Live Interactive Engine	Junyan Wang, Peng Zhang, Cheng Zhang, Dawei Song	In this paper, we present a novel Synchronous Collaborative Search System with a Live Interactive Engine (SCSS-LIE).
200	Information Retrieval Meets Scalable Text Analytics: Solr Integration with Spark	Ryan Clancy, Jaejun Lee, Zeynep Akkalyoncu Yilmaz, Jimmy Lin	This demonstration explores exactly such an integration: we evaluate performance under different analytical scenarios and present three simple case studies that illustrate the range of possible analyses enabled by seamlessly connecting Spark to Solr.
201	Social Knowledge Graph Explorer	Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay	In this paper we describe the main components of the system and showcase some examples.
202	cwl_eval: An Evaluation Tool for Information Retrieval	Leif Azzopardi, Paul Thomas, Alistair Moffat	We present a tool (“cwl_eval”) which unifies many metrics typically used to evaluate information retrieval systems using test collections.
203	TrecTools: an Open-source Python Library for Information Retrieval Practitioners Involved in TREC-like Campaigns	Jo?o Palotti, Harrisen Scells, Guido Zuccon	This paper introduces TrecTools, a Python library for assisting Information Retrieval (IR) practitioners with TREC-like campaigns.
204	Automatic Curation of Content Tables for Educational Videos	Arpan Mukherjee, Shubhi Tiwari, Tanya Chowdhury, Tanmoy Chakraborty	We present a novel architecture to curate content tables for educational videos.
205	Explanatory and Actionable Debugging for Machine Learning: A TableQA Demonstration	Minseok Cho, Gyeongbok Lee, Seung-won Hwang	The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging.
206	A Pipeline for Disaster Response and Relief Coordination	Pranav Maneriker, Nikhita Vedula, Hussein S. Al-Olimat, Jiayong Liang, Omar El-Khoury, Ethan Kubatko, Desheng Liu, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth, Srinivasan Parthasarathy	We present these results in a modularized, interactive map-based visualization, which can help emergency responders to better plan and coordinate disaster response.
207	Event Tracker: A Text Analytics Platform for Use During Disasters	Charles Thomas, Richard McCreadie, Iadh Ounis	In particular, Event Tracker provides a series of novel functionalities to realise this unified view of the event, namely: real-time identification of critical information, automatic grouping of content by the information needs of response officers, as well as real-time volunteers management and communication.
208	KANDINSKY: Abstract Art-Inspired Visualization of Social Discussions	Christina Lui, Sourav S. Bhowmick, Adam Jatowt	In this demonstration, we present a novel end-to-end visualization system called Kandinsky to supportmulti-faceted visualization of social discussions associated with an anchor post.
209	EXACT: Attributed Entity Extraction By Annotating Texts	Ke Chen, Lei Feng, Qingkuang Chen, Gang Chen, Lidan Shou	We propose a system called EXACT for extracting attributed entities from textual documents by performing explorative annotation tasks, which create attributes and bind them to tag values.
210	Expert-Guided Entity Extraction using Expressive Rules	Mayank Kejriwal, Runqi Shao, Pedro Szekely	In this demonstration paper, we present a system that allows domain experts to construct knowledge graphs by writing sophisticated rule-based entity extractors with minimal training, using a GUI-based editor that offers a range of complex facilities.
211	An Experimentation Platform for Precision Medicine	Vincent Nguyen, Sarvnaz Karimi, Brian Jin	We present information retrieval researchers with an on-line system which enables experimentation in search for precision medicine within the framework provided by the TREC Precision Medicine (PM) track.
212	WestSearch Plus: A Non-factoid Question-Answering System for the Legal Domain	Gayle McElvain, George Sanchez, Sean Matthews, Don Teo, Filippo Pompili, Tonya Custis	We present a non-factoid QA system that provides legally accurate, jurisdictionally relevant, and conversationally responsive answers to user-entered questions in the legal domain.
213	Demonstrating Requirement Search on a University Degree Search Application	Nicholas Mendez, Kyle De Freitas, Inzamam Rahaman	In this paper, we propose RevBoMIR, which utilizes a modified Boolean Model for Information Retrieval to retrieve requirements-based documents without sacrificing the expressiveness of requirements.
214	Alexa, Can You Help Me Shop?	Yoelle Maarek	In this talk, we will discuss in more details the key stages required in addressing customer’s needs, namely (1) understanding customers, (2) satisfying their needs, and (3) predicting them.
215	Challenges in Search on Streaming Services: Netflix Case Study	Sudarshan Lamkhede, Sudeep Das	We discuss salient challenges of building a search experience for a streaming media service such as Netflix.
216	Challenges and Opportunities in Understanding Spoken Queries Directed at Modern Entertainment Platforms	Ferhan Ture, Jinfeng Rao, Raphael Tang, Jimmy Lin	At a high level, our goal is to provide natural speech-based access to these myriad features as an alternative to physical button entry on a remote control.
217	Ghosting: Contextualized Query Auto-Completion on Amazon Search	Lakshmi Ramachandran, Uma Murthy	We present a behavioral recommendation model that uses customer search context to ghost on high-confidence queries.
218	Simulacra and Selection: Clothing Set Recommendation at Stitch Fix	Kevin Zielnicki	While such methods typically just augment a traditional browsing experience, Stitch Fix goes a step further by exclusively delivering curated selections of items, via algorithmically-assisted stylist recommendations.
219	Searching for Communities: a Facebook Way	Viet Ha-Thuc, Srinath Aaleti, Rongda Zhu, Nade Sritanyaratana, Corey Chen	This talk discusses these challenges in depth and describes our solution.
220	From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Search	Rui Li, Yunjiang Jiang, Wenyun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, Eric Yihong Zhao	We introduce deep learning models to the two most important stages in product search at JD.com, one of the largest e-commerce platforms in the world.
221	Beyond Keyword Targeting: An End-to-End Ad Retrieval Framework for Sponsored Search	Xiao Yang, Zhi Guo, Zongyao Ding	In this paper, we present an end-to-end ad retrieval framework for sponsored search.
222	Nobody Said it Would be Easy: A Decade of R&D Projects in Information Access from Thomson over Reuters to Refinitiv	Jochen L. Leidner	In this talk, I survey a small, non-random sample of research projects in information access carried out as part of the Thomson Reuters family of companies over the course of a 10+-year period.
223	Family History Discovery through Search at Ancestry	Peng Jiang, Yingrui Yang, Gann Bierner, Fengjie Alex Li, Ruhan Wang, Azadeh Moghtaderi	At Ancestry, we apply learning to rank algorithms to a new area to assist our customers in better understanding their family history.
224	USEing Transfer Learning in Retrieval of Statistical Data	Anton Firsov, Vladimir Bugay, Anton Karpenko	Following this trend, we combined DSSM-like architecture with USE (Universal Sentence Encoder) and BERT (Bidirectional Encoder Representations from Transformers) models in order to be able to fine-tune them on a small amount of click-through data and use them for information retrieval.
225	Find Relevant Cases in All Cases: Your Journey at Doctrine	Nicolas Fiorini	In this presentation, we provide some intuition regarding the specificities of legal IR (e.g., what is relevance?), and we introduce some of the solutions currently used on doctrine.fr.
226	Non-factoid Question Answering in the Legal Domain	Gayle McElvain, George Sanchez, Don Teo, Tonya Custis	We present work done on a QA system that is entirely based on IR and NLP, and does not rely on a structured knowledge base.
227	Looking for Opportunities: Challenges in Procurement Search	Stuart Mackie, David Macdonald, Leif Azzopardi, Yashar Moshfeghi	In this talk, we will provide an overview of procurement search and then describe the challenges in addressing the related search and recommendation tasks.
228	Dynamic Content Monitoring and Exploration using Vector Spaces	Benyou Wang	This doctoral research project investigates using Quantum Theory (QT) to represent language, especially in some dynamic scenarios, e.g. when dealing with dynamic corpora or interactive tasks.
229	Characterizing the Stages of Complex Tasks	Jiqun Liu	To address this issue at both theoretical and empirical levels, my dissertation aims to construct an explainable framework that can characterize the stages or states of complex search tasks over multiple dimensions and to apply the framework in proactive search path evaluation and recommendation.
230	Ranking Robustness In Adversarial Retrieval Settings	Gregory Goren	We presented a formalism of different notions of ranking robustness that gave rise to a few theoretical findings.
231	Event Information Retrieval from Text	Rashmi Sankepally	The goal of this work is to develop effective information retrieval systems that can help users to satisfy event-related information needs.
232	Implicit Entity Recognition, Classification and Linking in Tweets	Hawre Hosseini	In other words, the objective of our work is to recognize and identify core entities of a tweet when those entities are not explicitly mentioned; this process is referred to as Implicit Named Entity Recognition and Linking.
233	Biomedical Heterogeneous Data Integration and Rank Retrieval using Data Bridges	Priya Deshpande	We propose building data bridges to support retrieving ranked relevant documents from integrated repository.
234	From Query Variations To Learned Relevance Modeling	Binsheng Liu	In this PhD project,we focus on automatic query variation generation and try to reduce the gap to human generated variations.We plan to leverage advances of transfer learning and classic relevance modeling to generate high quality query variations to improve system performance.
235	Multimodal Data Fusion with Quantum Inspiration	Qiuchi Li	To answer this question, we propose to fuse multimodal data with complex-valued neural networks, motivated by the theoretical link between neural networks and quantum theory [4] and advances in complex-valued neural networks [9].
236	Document Distance Metric Learning in an Interactive Exploration Process	Marco Wrzalik	Therefore, this research proposes to investigate online adjustments to the similarity model using feedback generated during exploration or exploratory search.
237	Measuring Job Search Effectiveness	Alfan Farizki Wicaksono	In this work, we propose methodologies and measures for evaluating the quality of job search rankings from a user modeling perspective.
238	Informing the Design of Conversational IR Systems: Framework and Result Presentation	Souvick Ghosh	We propose a study to evaluate the users’ preference of modalities when using conversational search systems.
239	Evaluating Risk-Sensitive Text Retrieval	Rodger Benham	Our research objectives are to: 1) Survey and revisit risk evaluation, taking into account frequentist and Bayesian inference approaches for comparing against multiple baselines; 2) Apply that new approach, evaluating a novel web search technique that leverages previously run queries to improve the effectiveness of a new user query; and 3) Explore how risk-sensitive component interactions affect end-to-end effectiveness in a search pipeline.
240	Efficient and Effective Text-Annotation through Active Learning	Markus Zlabinger	In my Ph.D. research, I address the two problems with the development of an unsupervised method for the computation of informative samples.
241	A Domain-Independent and Multilingual Approach for Crisis Event Detection and Understanding	Hernan Sarmiento	The main objective of this work is to study and exploit cross-lingual domain-independent patterns for detecting and characterizing social media messages generated in collective activity related to unexpected high-impact real-world events in social media platforms, and specifically on emergency situations.