Paper Digest: CIKM 2019 Highlights

November 1, 2019November 2, 2019 admin

The ACM Conference on Information and Knowledge Management (CIKM) is an annual computer science research conference dedicated to information management and knowledge management. In 2019, it is to be held in Beijing, China. There were 1,031 paper submissions, of which 202 were accepted as long papers.

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.

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Paper Digest Team
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TABLE 1: CIKM 2019 Long Papers

	Title	Authors	Highlight
1	On VR Spatial Query for Dual Entangled Worlds	Shao-Heng Ko, Ying-Chun Lin, Hsu-Chao Lai, Wang-Chien Lee, De-Nian Yang	We prove DROP is NP-hard and design a fully polynomial-time approximation scheme, Dual Entangled World Navigation (DEWN), by finding Minimum Immersion Loss Range (MIL Range).
2	Sketching Streaming Histogram Elements using Multiple Weighted Factors	Quang-Huy Duong, Heri Ramampiaro, Kjetil N?rv?g	We propose a novel sketching approach for streaming data that, even with limited computing resources, enables processing high volume and high velocity data efficiently.
3	Improved Compressed String Dictionaries	Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Gonzalo Navarro	We introduce a new family of compressed data structures to efficiently store and query large string dictionaries in main memory.
4	On Transforming Relevance Scales	Lei Han, Kevin Roitero, Eddy Maddalena, Stefano Mizzaro, Gianluca Demartini	In this paper we look at the effect of scale transformations in a systematic way.
5	Streamline Density Peak Clustering for Practical Adoptions	Shuai Yang, Xipeng Shen, Min Chi	This work proposes an improved algorithm named Streamlined Density Peak Clustering (SDPC).
6	Recommendation-based Team Formation for On-demand Taxi-calling Platforms	Lingyu Zhang, Tianshu Song, Yongxin Tong, Zimu Zhou, Dan Li, Wei Ai, Lulu Zhang, Guobin Wu, Yan Liu, Jieping Ye	In this paper, we propose to form teams among drivers to promote participation.
7	DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation	Tao-yang Fu, Wang-Chien Lee	To fill in this gap, in this work, we propose a novel neural network framework, namely Deep Image-based Spatio-Temporal network (DeepIST), for travel time estimation of a given path.
8	Personalized Route Description Based On Historical Trajectories	Han Su, GuangLin Cong, Wei Chen, Bolong Zheng, Kai Zheng	In this paper, we study a Personalized Route Description system dubbed PerRD-with which the goal is to generate more customized and intuitive route descriptions based on user generated content.
9	Geolocating Tweets in any Language at any Location	Mike Izbicki, Vagelis Papalexakis, Vassilis Tsotras	This paper introduces the Unicode Convolutional Neural Network (UnicodeCNN) for analyzing text written in any language.
10	SeiSMo: Semi-supervised Time Series Motif Discovery for Seismic Signal Detection	M Ashraf Siddiquee, Zeinab Akhavan, Abdullah Mueen	We propose a semi-supervised motif discovery algorithm that forms a nearest neighbor graph to identify chains of nearest neighbors from the given events.
11	UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction	Qingxiong Tan, Andy Jinhua Ma, Mang Ye, Baoyao Yang, Huiqi Deng, Vincent Wai-Sun Wong, Yee-Kit Tse, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Jessica Yuet-Ling Ching, Francis Ka-Leung Chan, Pong C. Yuen	In this paper, we propose a novel Uncertainty-Aware Convolutional Recurrent Neural Network (UA-CRNN), which incorporates the uncertainty information in the generated data to improve the mortality risk prediction performance.
12	Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images	Changhee Han, Kohei Murao, Tomoyuki Noguchi, Yusuke Kawata, Fumiya Uchiyama, Leonardo Rundo, Hideki Nakayama, Shin’ichi Satoh	Therefore, this paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 ? 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic bounding box annotation improves the training robustness.
13	Domain Knowledge Guided Deep Atrial Fibrillation Classification and Its Visual Interpretation	Xiaoyu Li, Buyue Qian, Jishang Wei, Xianli Zhang, Sirui Chen, Qinghua Zheng, none none	To alleviate such limitation, we are bringing the best from the two worlds and propose a domain knowledge guided deep neural network.
14	Question Difficulty Prediction for Multiple Choice Problems in Medical Exams	Zhaopeng Qiu, Xian Wu, Wei Fan	In this paper, we propose a Document enhanced Attention based neural Network(DAN) framework to predict the difficulty of multiple choice problems in medical exams.
15	GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment	Sendong Zhao, Chang Su, Andrea Sboner, Fei Wang	In this paper, we propose GRAPHENE,which is a deep learning based framework for precise BLR.
16	Video-level Multi-model Fusion for Action Recognition	Xiaomin Wang, Junsan Zhang, Leiquan Wang, Philip S. Yu, Jie Zhu, Haisheng Li	In this paper, a video-level multi-model fusion action recognition method is proposed to solve these problems.
17	Large Scale Landmark Recognition via Deep Metric Learning	Andrei Boiarov, Eduard Tyantov	This paper presents a novel approach for landmark recognition in images that we’ve successfully deployed at Mail.ru.
18	Multi-stage Deep Classifier Cascades for Open World Recognition	Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng, Liang Zhao	Thus, to address the above challenges, we propose a novel generic end-to-end framework consisting of a dynamic cascade of classifiers that incrementally learn their dynamic and inherent features.
19	Inferring Context from Pixels for Multimodal Image Classification	Manan Shah, Krishnamurthy Viswanathan, Chun-Ta Lu, Ariel Fuxman, Zhen Li, Aleksei Timofeev, Chao Jia, Chen Sun	We propose a framework that consists of two main components: (1) a phrase generator that maps image pixels to a contextual phrase, and (2) a multimodal model that uses textual features from the phrase generator and visual features from the image pixels to produce labels in the output taxonomy.
20	Multi-Target Multi-Camera Tracking with Human Body Part Semantic Features	Mingkun Wang, Dianxi Shi, Naiyang Guan, Wei Yi, Tao Zhang, Zunlin Fan	We propose MTMCT\_HS which uses human body part semantic features to overcome the above challenges.
21	Efficient Join Processing Over Incomplete Data Streams	Weilong Ren, Xiang Lian, Kambiz Ghazinour	In this paper, we formalize an important problem, namely join over incomplete data streams (Join-iDS), which retrieves joining object pairs from incomplete data streams with high confidences.
22	Inclusion Dependency Discovery: An Experimental Evaluation of Thirteen Algorithms	Falco D?rsch, Axel Stebner, Fabian Windheuser, Maxi Fischer, Tim Friedrich, Nils Strelow, Tobias Bleifu?, Hazar Harmouch, Lan Jiang, Thorsten Papenbrock, Felix Naumann	This paper summarizes the different state-of-the-art discovery approaches and discusses their commonalities.
23	Constructing a Comprehensive Events Database from the Web	Qifan Wang, Bhargav Kanagal, Vijay Garg, D. Sivakumar	In this paper, we consider the problem of constructing a comprehensive database of events taking place around the world.
24	Deploying Hash Tables on Die-Stacked High Bandwidth Memory	Xuntao Cheng, Bingsheng He, Eric Lo, Wei Wang, Shengliang Lu, Xinyu Chen	In this work, we propose a deployment algorithm that first estimates the memory access cost and then places data in a way that exploits the hybrid memory architecture in a balanced manner.
25	Partially Shared Adversarial Learning For Semi-supervised Multi-platform User Identity Linkage	Chaozhuo Li, Senzhang Wang, Hao Wang, Yanbo Liang, Philip S. Yu, Zhoujun Li, Wei Wang	In this paper, we propose a novel adversarial learning based framework MSUIL with partially shared generators to perform Semi-supervised User Identity Linkage across Multiple social networks.
26	Adversarial Domain Adaptation with Semantic Consistency for Cross-Domain Image Classification	Manliang Cao, Xiangdong Zhou, Yiming Xu, Yue Pang, Bo Yao	In this paper, we propose Adversarial Domain Adaptation with Semantic Consistency (ADASC) model to align the discriminative features across domains progressively and effectively, via exploiting the class-level relations between domains.
27	ATL: Autonomous Knowledge Transfer from Many Streaming Processes	Mahardhika Pratama, Marcus de Carvalho, Renchunzi Xie, Edwin Lughofer, Jie Lu	Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes.
28	Knowledge Transfer based on Multiple Manifolds Assumption	Pengfei Wei, Yiping Ke	In this paper, we propose to transfer knowledge across domains under the multiple manifolds assumption that assumes the data are sampled from multiple low-dimensional manifolds.
29	Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning	Zhuoren Jiang, Jian Wang, Lujun Zhao, Changlong Sun, Yao Lu, Xiaozhong Liu	In this study, we propose a novel problem, cross-domain aspect category transfer and detection, which faces three challenges: various feature spaces, different data distributions, and diverse output spaces.
30	A Deep Neural Framework for Sales Forecasting in E-Commerce	Yan Qi, Chenliang Li, Han Deng, Min Cai, Yunwei Qi, Yuming Deng	In this paper, we propose a novel deep neural framework for sales forecasting in E-Commerce, named DSF.
31	An Active and Deep Semantic Matching Framework for Query Rewrite in E-Commercial Search Engine	Yatao Yang, Jun Tan, Hongbo Deng, Zibin Zheng, Yutong Lu, Xiangke Liao	In order to address the above challenges, we propose an active and deep semantic matching framework (ActiveMatch) which is composed of two components.
32	AIBox: CTR Prediction Model Training on a Single Node	Weijie Zhao, Jingyuan Zhang, Deping Xie, Yulei Qian, Ronglai Jia, Ping Li	This paper presents AIBox, a centralized system to train CTR models with tens-of-terabytes-scale parameters by employing solid-state drives (SSDs) and GPUs.
33	Improving Ad Click Prediction by Considering Non-displayed Events	Bowen Yuan, Jui-Yang Hsia, Meng-Yuan Yang, Hong Zhu, Chih-Yao Chang, Zhenhua Dong, Chih-Jen Lin	In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system.
34	Approximation Algorithms for Coordinating Ad Campaigns on Social Networks	Kartik Lakhotia, David Kempe	We study a natural model of coordinated social ad campaigns over a social network, based on models of Datta et al. and Aslay et al.
35	Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction	Yikai Wang, Liang Zhang, Quanyu Dai, Fuchun Sun, Bo Zhang, Yang He, Weipeng Yan, Yongjun Bao	As existing CTR prediction works neglect the importance of the temporal signals when embed users’ historical clicking records, we propose a time-aware attention model which explicitly uses absolute temporal signals for expressing the users’ periodic behaviors and relative temporal signals for expressing the temporal relation between items.
36	Conversational Product Search Based on Negative Feedback	Keping Bi, Qingyao Ai, Yongfeng Zhang, W. Bruce Croft	So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration.
37	Learning to Ask: Question-based Sequential Bayesian Product Search	Jie Zou, Evangelos Kanoulas	In this paper, we propose a novel interactive method to effectively locate the best matching product.
38	A Zero Attention Model for Personalized Product Search	Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, W. Bruce Croft	Based on these observations, we propose a Zero Attention Model for product search that automatically determines when and how to personalize a user-query pair via a novel attention mechanism.
39	Learning to Generate Personalized Product Descriptions	Guy Elad, Ido Guy, Slava Novgorodov, Benny Kimelfeld, Kira Radinsky	In this work, we propose an approach to personalize product descriptions according to the personality of an individual user.
40	Fast and Accurate Network Embeddings via Very Sparse Random Projection	Haochen Chen, Syed Fahad Sultan, Yingtao Tian, Muhao Chen, Steven Skiena	We present FastRP, a scalable and performant algorithm for learning distributed node representations in a graph.
41	Hierarchical Community Structure Preserving Network Embedding: A Subspace Approach	Qingqing Long, Yiming Wang, Lun Du, Guojie Song, Yilun Jin, Wei Lin	In this paper, we propose our network embedding framework, abbreviated SpaceNE, preserving hierarchies formed by communities through subspaces, manifolds with flexible dimensionalities and are inherently hierarchical.
42	Collective Link Prediction Oriented Network Embedding with Hierarchical Graph Attention	Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yangyong Zhu	In this paper, we target on the collective link prediction problem and aim to predict both the intra-network social links as well as the inter-network anchor links across multiple aligned social networks.
43	Discerning Edge Influence for Network Embedding	Yaojing Wang, Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu	In this paper, we propose to discern the edge influence as the first step towards understanding skip-gram basd network embedding methods.
44	Constrained Co-embedding Model for User Profiling in Question Answering Communities	Yupeng Luo, Shangsong Liang, Zaiqiao Meng	In this paper, we study the problem of user profiling in question answering communities.
45	Hyper-Path-Based Representation Learning for Hyper-Networks	Jie Huang, Xin Liu, Yangqiu Song	In this paper, we firstly define a metric to depict the degrees of indecomposability for hyper-networks. Then we propose a new concept called hyper-path and design hyper-path-based random walks to preserve the structural information of hyper-networks according to the analysis of the indecomposability.
46	Multi-Hot Compact Network Embedding	Chaozhuo Li, Lei Zheng, Senzhang Wang, Feiran Huang, Philip S. Yu, Zhoujun Li	In this paper we propose a novel multi-hot compact network embedding framework to effectively reduce memory cost by learning partially shared embeddings.
47	Temporal Network Embedding with Micro- and Macro-dynamics	Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye	In this paper, we propose a novel temporal network embedding method with micro- and macro-dynamics, named $\rmM^2DNE $.
48	MrMine: Multi-resolution Multi-network Embedding	Boxin Du, Hanghang Tong	In this paper, we propose a unified framework MrMine to learn the representations of objects from multiple networks at three complementary resolutions (i.e., network, subgraph and node) simultaneously.
49	Task-Guided Pair Embedding in Heterogeneous Network	Chanyoung Park, Donghyun Kim, Qi Zhu, Jiawei Han, Hwanjo Yu	In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e.g., paper-author relationship in author identification).
50	Graph Convolutional Networks with Motif-based Attention	John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao	In this work, we propose a motif-based graph attention model, called Motif Convolutional Networks, which generalizes past approaches by using weighted multi-hop motif adjacency matrices to capture higher-order neighborhoods.
51	Long-tail Hashtag Recommendation for Micro-videos with Graph Convolutional Network	Mengmeng Li, Tian Gan, Meng Liu, Zhiyong Cheng, Jianhua Yin, Liqiang Nie	In this paper, we recommend hashtags for micro-videos by presenting a novel multi-view representation interactive embedding model with graph-based information propagation.
52	Hashing Graph Convolution for Node Classification	Wenting Zhao, Zhen Cui, Chunyan Xu, Chengzheng Li, Tong Zhang, Jian Yang	To address this problem, in this paper, we propose a simple but effective Hashing Graph Convolution (HGC) method by using global-hashing and local-projection on node aggregation for the task of node classification.
53	Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation	Fengli Xu, Jianxun Lian, Zhenyu Han, Yong Li, Yujian Xu, Xing Xie	To address these problems, we propose RecoGCN, which stands for a RElation-aware CO-attentive GCN model, to effectively aggregate heterogeneous features in a HIN.
54	Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction	Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, Liang Wang	In this work, we propose to represent the multi-field features in a graph structure intuitively, where each node corresponds to a feature field and different fields can interact through edges.
55	Key Player Identification in Underground Forums over Attributed Heterogeneous Information Network Embedding Framework	Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Chuan Shi	In order to combat the evolving cybercrimes, in this paper, we propose and develop an intelligent system named iDetective to automate the analysis of underground forums for the identification of key players (i.e., users who play the vital role in the value chain).
56	Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach	Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, Zhong Liu	In this paper, we focus on the efficient identification of top-k nodes with highest BC in a graph, which is an essential task to many network applications.
57	Multiple Rumor Source Detection with Graph Convolutional Networks	Ming Dong, Bolong Zheng, Nguyen Quoc Viet Hung, Han Su, Guohui Li	In this paper, we propose a deep learning based model, namely GCNSI (Graph Convolutional Networks based Source Identification), to locate multiple rumor sources without prior knowledge of underlying propagation model.
58	Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks	Ruihong Qiu, Jingjing Li, Zi Huang, Hongzhi YIn	In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.
59	Gravity-Inspired Graph Autoencoders for Directed Link Prediction	Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet-Anh Tran, Michalis Vazirgiannis	In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs.
60	Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity	Yu Shi, Jiaming Shen, Yuchen Li, Naijing Zhang, Xinwei He, Zhengzhi Lou, Qi Zhu, Matthew Walker, Myunghwan Kim, Jiawei Han	In this work, we propose to discover hypernymy in text-rich HINs, which can introduce additional high-quality signals.
61	aCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model	Shifu Hou, Yujie Fan, Yiming Zhang, Yanfang Ye, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao	In this paper, we explore the robustness of HG based model in Android malware detection at the first attempt.
62	BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network	Seonghyeon Lee, Chanyoung Park, Hwanjo Yu	In this paper, we devise a new heterogeneous network embedding method, called BHIN2vec, which considers the balance among all relation types in a network.
63	Deep Sequence-to-Sequence Entity Matching for Heterogeneous Entity Resolution	Hao Nie, Xianpei Han, Ben He, Le Sun, Bo Chen, Wei Zhang, Suhui Wu, Hao Kong	In this paper, we propose a deep sequence-to-sequence entity matching model, denoted Seq2SeqMatcher, which can effectively solve the heterogeneous and dirty problems by modeling ER as a token-level sequence-to-sequence matching task.
64	HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding	Yu He, Yangqiu Song, Jianxin Li, Cheng Ji, Jian Peng, Hao Peng	In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process,and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes.
65	EHR Coding with Multi-scale Feature Attention and Structured Knowledge Graph Propagation	Xiancheng Xie, Yun Xiong, Philip S. Yu, Yangyong Zhu	In this paper, we leverage a densely connected convolutional neural network which is able to produce variable n-gram features for clinical note feature learning.
66	A Fine-grained and Noise-aware Method for Neural Relation Extraction	Jianfeng Qu, Wen Hua, Dantong Ouyang, Xiaofang Zhou, Ximing Li	To address these problems, we propose two reasonable assumptions and craft reinforcement learning to capture the expressive sentence for each relation mentioned in a bag.
67	Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations	Xiongnan Jin, Byungkook Oh, Sanghak Lee, Dongho Lee, Kyong-Ho Lee, Liang Chen	In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS.
68	Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks	Yuting Ye, Xuwu Wang, Jiangchao Yao, Kunyang Jia, Jingren Zhou, Yanghua Xiao, Hongxia Yang	Here we present BEM, a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs.
69	A Benchmark for Fact Checking Algorithms Built on Knowledge Bases	Viet-Phi Huynh, Paolo Papotti	We introduce a benchmark framework to systematically enforce such properties in training and testing datasets with fine tune control over their properties.
70	Online Schemaless Querying of Heterogeneous Open Knowledge Bases	Nikita Bhutani, H V Jagadish	In this paper, we introduce an online schemaless querying method that does not require the query to exactly match the facts.
71	Enhancing Conversational Dialogue Models with Grounded Knowledge	Wen Zheng, Ke Zhou	Therefore, in this paper, we aim to bridge the gap by first extensively evaluating various types of state-of-the-art knowledge-grounded conversational models, including recurrent neural network based, memory networks based, and Transformer based models.
72	MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data	Yang Deng, Yaliang Li, Ying Shen, Nan Du, Wei Fan, Min Yang, Kai Lei	In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs.
73	Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion	Philipp Christmann, Rishiraj Saha Roy, Abdalghani Abujabal, Jyotsna Singh, Gerhard Weikum	As a solution, we develop CONVEX, an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions.
74	Learning to Answer Complex Questions over Knowledge Bases with Query Composition	Nikita Bhutani, Xinyi Zheng, H V Jagadish	We propose a KB-QA system, TextRay, which answers complex questions using a novel decompose-execute-join approach.
75	Auto-completion for Data Cells in Relational Tables	Shuo Zhang, Krisztian Balog	We present the CellAutoComplete framework to tackle several novel aspects of this problem, including: (i) enabling a cell to have multiple, possibly conflicting values, (ii) supplementing the predicted values with supporting evidence, (iii) combining evidence from multiple sources, and (iv) handling the case where a cell should be left empty.
76	Author Set Identification via Quasi-Clique Discovery	Yuyan Zheng, Chuan Shi, Xiangnan Kong, Yanfang Ye	In this paper, we take the relationships among authors into consideration to study the problem of author set identification, which is to identify an author set rather than an individual author related to an anonymous paper.
77	AdaFair: Cumulative Fairness Adaptive Boosting	Vasileios Iosifidis, Eirini Ntoutsi	To this end, we propose AdaFair, a fairness-aware classifier based on AdaBoost that further updates the weights of the instances in each boosting round taking into account a cumulative notion of fairness based upon all current ensemble members, while explicitly tackling class-imbalance by optimizing the number of ensemble members for balanced classification error.
78	New Online Kernel Ridge Regression via Incremental Predictive Sampling	Shan Xu, Xiao Zhang, Shizhong Liao	In this paper, we propose a new online kernel ridge regression via an incremental predictive sampling approach, which has the nearly optimal accumulated loss and performs efficiently at each round.
79	Online Kernel Selection via Tensor Sketching	Shizhong Liao, Xiao Zhang	To address these issues, we propose a novel online kernel selection approach using tensor sketching, which has constant computational complexities at each round and enjoys a sublinear regret bound for an uncountably infinite number of candidate kernels.
80	One-Class Active Learning for Outlier Detection with Multiple Subspaces	Holger Trittenbach, Klemens B?hm	To overcome it, we propose SubSVDD, a semi-supervised classifier, that learns decision boundaries in low-dimensional projections of the data.
81	AutoGRD: Model Recommendation Through Graphical Dataset Representation	Noy Cohen-Shapira, Lior Rokach, Bracha Shapira, Gilad Katz, Roman Vainshtein	We present AutoGRD, a novel meta-learning approach for algorithm recommendation.
82	Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection	Yao Tan, Liu Yang, Qinghua Hu, Zhibin Du	In this work, we propose a general batch mode active learning algorithm for semantic segmentation which automatically selects important samples to be labeled for building a competitive classifier.
83	CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction	Theodoros Rekatsinas, Amol Deshpande, Aditya Parameswaran	We design an adaptive querying framework, CRUX, that maximizes the number of extracted entities for a given budget.
84	Deep Forest with LRRS Feature for Fine-grained Website Fingerprinting with Encrypted SSL/TLS	Ziqing Zhang, Cuicui Kang, Gang Xiong, Zhen Li	To solve the feature compatibility problem, we propose to use the local request and response sequence (LRRS) as features.
85	N2N: Network Derivative Mining	Jian Kang, Hanghang Tong	In this paper, we introduce network derivative mining problem.
86	MoBoost: A Self-improvement Framework for Linear-based Hashing	Xingbo Liu, Xiushan Nie, Xiaoming Xi, Lei Zhu, Yilong Yin	In this study, we propose a novel generalized framework called Model Boost (MoBoost), which can achieve the self-improvement of the linear-based hashing.
87	Loopless Semi-Stochastic Gradient Descent with Less Hard Thresholding for Sparse Learning	Xiangyang Liu, Bingkun Wei, Fanhua Shang, Hongying Liu	To be more efficient in large-scale datasets, we propose an efficient single-layer semi-stochastic gradient hard thresholding (LSSG-HT) method.
88	EPA: Exoneration and Prominence based Age for Infection Source Identification	Syed Shafat Ali, Tarique Anwar, Ajay Rastogi, Syed Afzal Murtaza Rizvi	In this paper, we study the problem by exploiting the idea of the source being the oldest node.
89	Generating Persuasive Visual Storylines for Promotional Videos	Chang Liu, Yi Dong, Han Yu, Zhiqi Shen, Zhanning Gao, Pan Wang, Changgong Zhang, Peiran Ren, Xuansong Xie, Lizhen Cui, Chunyan Miao	To address this problem, we propose WundtBackpack, an algorithmic approach to generate storylines based on available visual materials, which can be video clips or images.
90	Clustering Recurrent and Semantically Cohesive Program Statements in Introductory Programming Assignments	Victor J. Marin, Carlos R. Rivero	This paper presents a data-driven approach for clustering recurrent program statements performing similar but not exact semantics across student programs, which we refer to as core statements.
91	Going Beyond Content Richness: Verified Information Aware Summarization of Crisis-Related Microblogs	Ashish Sharma, Koustav Rudra, Niloy Ganguly	In this paper, we work on the novel task of generating verified summaries of information posted on Twitter during disasters.
92	Declarative User Selection with Soft Constraints	Yael Amsterdamer, Tova Milo, Amit Somech, Brit Youngmann	To this end, we introduce a novel declarative framework that abstracts common components of the user selection problem, while allowing for domain-specific tuning.
93	#suicidal – A Multipronged Approach to Identify and Explore Suicidal Ideation in Twitter	Pradyumna Prakhar Sinha, Rohan Mishra, Ramit Sawhney, Debanjan Mahata, Rajiv Ratn Shah, Huan Liu	In this work we focus on identifying suicidal posts from Twitter.
94	MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction	Yucheng Jin, Wanling Cai, Li Chen, Nyi Nyi Htun, Katrien Verbert	Therefore, we present MusicBot, a chatbot for music recommendations, featured with two typical critiquing techniques, user-initiated critiquing (UC) and system-suggested critiquing (SC).
95	Discovering Polarized Communities in Signed Networks	Francesco Bonchi, Edoardo Galimberti, Aristides Gionis, Bruno Ordozgoiti, Giancarlo Ruffo	In this paper we consider the problem of discovering polarized communities in signed networks.
96	Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing	Shuai Xiao, Le Guo, Zaifan Jiang, Lei Lv, Yuanbo Chen, Jun Zhu, Shuang Yang	In this paper, we formulate the problem as a constrained Markov decision process (CMDP).
97	Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures	Parisa Kaghazgaran, Majid Alfifi, James Caverlee	In this work, we propose and evaluate a new class of attacks on online review platforms based on neural language models at word-level granularity in an inductive transfer-learning framework wherein a universal model is refined to handle domain shift, leading to potentially wide-ranging attacks on review systems.
98	Augment to Prevent: Short-Text Data Augmentation in Deep Learning for Hate-Speech Classification	Georgios Rizos, Konstantin Hemker, Bj?rn Schuller	In this paper, we address the issue of augmenting text data in supervised Natural Language Processing problems, exemplified by deep online hate speech classification.
99	Nested Relation Extraction with Iterative Neural Network	Yixuan Cao, Dian Chen, Hongwei Li, Ping Luo	In this paper, we formally formulate the nested relation extraction problem, and come up with a solution using Iterative Neural Network.
100	Learning Chinese Word Embeddings from Stroke, Structure and Pinyin of Characters	Yun Zhang, Yongguo Liu, Jiajing Zhu, Ziqiang Zheng, Xiaofeng Liu, Weiguang Wang, Zijie Chen, Shuangqing Zhai	In this paper, we design feature substring, a super set of radicals, components and stroke n-gram with structure and pinyin information, to integrate stroke, structure and pinyin features of Chinese characters and capture the semantics of Chinese words.
101	Sentiment Commonsense Induced Sequential Neural Networks for Sentiment Classification	Chen Shiyun, Lin Xin, Xiao Yanghua, He Liang	In this paper, we propose an auxiliary tagging task to integrate sentiment commonsense into sequential neural networks (such as LSTM).
102	Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis	Da Yin, Xiao Liu, Xiaojun Wan	In this paper, we propose an interactive multi-grained joint model for targeted sentiment analysis.
103	Beyond word2vec: Distance-graph Tensor Factorization for Word and Document Embeddings	Suhang Wang, Charu Aggarwal, Huan Liu	In this paper, we present a tensor factorization methodology, which simultaneously embeds words and sentences into latent representations in one shot.
104	Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach	Wei Huang, Enhong Chen, Qi Liu, Yuying Chen, Zai Huang, Yang Liu, Zhou Zhao, Dan Zhang, Shijin Wang	To that end, in this paper, we propose a novel framework called Hierarchical Attention-based Recurrent Neural Network (HARNN) for classifying documents into the most relevant categories level by level via integrating texts and the hierarchical category structure.
105	A Semantics Aware Random Forest for Text Classification	Md Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu, Wei Kang	This paper is among these approaches. It proposes a Semantics Aware Random Forest (SARF) classifier. SARF extracts the features used by trees to generate the predictions and selects a subset of the predictions for which the features are relevant to the predicted classes.
106	Federated Topic Modeling	Di Jiang, Yuanfeng Song, Yongxin Tong, Xueyang Wu, Weiwei Zhao, Qian Xu, Qiang Yang	In this paper, we propose a novel framework named Federated Topic Modeling (FTM), in which multiple parties collaboratively train a high-quality topic model by simultaneously alleviating data scarcity and maintaining immune to privacy adversaries.
107	Multi-Turn Response Selection in Retrieval-Based Chatbots with Iterated Attentive Convolution Matching Network	Heyuan Wang, Ziyi Wu, Junyu Chen	In this paper, we investigate selecting the proper response for a context through multi-grained representation and interactive matching.
108	Sentiment Lexicon Enhanced Neural Sentiment Classification	Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang, Xing Xie	In this paper, we propose two approaches to exploit sentiment lexicons to enhance neural sentiment classification.
109	ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning	Yong Luo, Huaizheng Zhang, Yonggang Wen, Xinwen Zhang	In this paper, we propose a novel framework that targets the same task, but integrate different types of information in a more sophisticated way and introduce adversarial learning to learn more expressive representation.
110	Regularizing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method	Shuai Yao, Yuexian Hou, Liangzhu Ge, Zeting Hu	Based on the theoretical analysis of generalization error of ensemble estimators (bias-variance-covariance decomposition), we find the variance of each base learner plays an important role in preventing overfitting and propose a novel regularizer—\emphEnsemble-based Low-Level Sample-Variances Method (ELSM) to encourage each base learner of hidden layers to have a low-level sample-variance.
111	Attention-Residual Network with CNN for Rumor Detection	Yixuan Chen, Jie Sui, Liang Hu, Wei Gong	In this paper, we propose an Attention-Residual network combined with CNN (ARC), which is based on the content features for rumor detection.
112	Imbalance Rectification in Deep Logistic Regression for Multi-Label Image Classification Using Random Noise Samples	Wenjin Yan, Ruixuan Li, Jun Wang, Yuhua Li, Jinyang Wang, Pan Zhou, Xiwu Gu	First, we find that feeding randomly generated noise samples into an LR classifier is an effective way to detect class imbalances, and further define an informative imbalance metric named inference tendency based on noise sample analysis. Second, we design an efficient moving average based method for calculating inference tendency, which can be easily done during training with negligible overhead. Third, two novel rectification methods called extremum shift (ES) and tendency constraint (TC) are designed to offset or constrain inference tendency in the loss function, and mitigate class imbalances significantly.
113	CamDrop: A New Explanation of Dropout and A Guided Regularization Method for Deep Neural Networks	Hongjun Wang, Guangrun Wang, Guanbin Li, Liang Lin	To force convolutional networks to explore more discriminative evidence throughout spatial regions, this paper presents a novel CamDrop to improve the conventional dropout in two aspects.
114	Dynamic Collaborative Recurrent Learning	Teng Xiao, Shangsong Liang, Zaiqiao Meng	In this paper, we provide a unified learning algorithm, dynamic collaborative recurrent learning, DCRL, of two directions of recommendations: temporal recommendations focusing on tracking the evolution of users’ long-term preference and sequential recommendations focusing on capturing short-term preferences given a short time window.
115	AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks	Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang	In this paper, we propose an effective and efficient method called the AutoInt to automatically learn the high-order feature interactions of input features.
116	Automatic Construction of Multi-layer Perceptron Network from Streaming Examples	Mahardhika Pratama, Choiru Za’in, Andri Ashfahani, Yew Soon Ong, Weiping Ding	A Neural Network with Dynamically Evolved Capacity (NADINE) is proposed in this paper.
117	Robust Embedded Deep K-means Clustering	Rui Zhang, Hanghang Tong, Yinglong Xia, Yada Zhu	To address this issue, we propose a robust embedded deep K-means clustering (RED-KC) method.
118	Discovering Interesting Cycles in Directed Graphs	Florian Adriaens, Cigdem Aslay, Tijl De Bie, Aristides Gionis, Jefrey Lijffijt	In this paper, we introduce the problem of discovering interesting cycles in graphs.
119	FLEET: Butterfly Estimation from a Bipartite Graph Stream	Seyed-Vahid Sanei-Mehri, Yu Zhang, Ahmet Erdem Sariy?ce, Srikanta Tirthapura	We present a space lower bound for any streaming algorithm that can estimate the number of butterflies accurately, as well as FLEET, a suite of algorithms for accurately estimating the number of butterflies in the graph stream.
120	Selecting the Optimal Groups: Efficiently Computing Skyline k-Cliques	Chen Zhang, Wenjie Zhang, Ying Zhang, Lu Qin, Fan Zhang, Xuemin Lin	Motivated by this, in this paper we formulate the novel model of skyline k-cliques over multi-valued attributed graphs and develop efficient algorithms to conduct the computation.
121	Balance in Signed Bipartite Networks	Tyler Derr, Cassidy Johnson, Yi Chang, Jiliang Tang	Therefore, in this work, we conduct the first comprehensive analysis and validation of balance theory using the smallest cycle in signed bipartite networks – signed butterflies (i.e., cycles of length 4 containing the two node types).
122	Adaptive Algorithms for Estimating Betweenness and k-path Centralities	Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem	In the current paper, first given a directed network G and a vertex $r\in V(G)$, we present a novel adaptive algorithm for estimating betweenness score of r.
123	Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments	Wenmian Yang, Weijia Jia, Wenyuan Gao, Xiaojie Zhou, Yutao Luo	In this paper, we proposed a novel Similarity-Based Network with Interactive Variance Attention (SBN-IVA) to classify comments as spoilers or not.
124	Detecting Malicious Accounts in Online Developer Communities Using Deep Learning	Qingyuan Gong, Jiayun Zhang, Yang Chen, Qi Li, Yu Xiao, Xin Wang, Pan Hui	In this work, we formulate the malicious account detection problem in online developer communities, and propose GitSec, a deep learning-based solution to detect malicious accounts.
125	Exploring Multi-Objective Exercise Recommendations in Online Education Systems	Zhenya Huang, Qi Liu, Chengxiang Zhai, Yu Yin, Enhong Chen, Weibo Gao, Guoping Hu	In this paper, we propose a novel Deep Reinforcement learning framework, namely DRE, for adaptively recommending Exercises to students with optimization of above three objectives.
126	Into the Battlefield: Quantifying and Modeling Intra-community Conflicts in Online Discussion	Subhabrata Dutta, Dipankar Das, Gunkirat Kaur, Shreyans Mongia, Arpan Mukherjee, Tanmoy Chakraborty	Our particular focus in this paper is to study conflict dynamics over online news articles in Reddit, one of the most popular online discussion platforms.
127	Offline and Online Satisfaction Prediction in Open-Domain Conversational Systems	Jason Ingyu Choi, Ali Ahmadvand, Eugene Agichtein	To accomplish this goal, we propose a conversational satisfaction prediction model specifically designed for open-domain spoken conversational agents, called ConvSAT.
128	Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis	Jing Ma, Qiuchen Zhang, Jian Lou, Joyce C. Ho, Li Xiong, Xiaoqian Jiang	We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
129	Achieve Privacy-Preserving Truth Discovery in Crowdsensing Systems	Jianchao Tang, ShaoJing Fu, Ming Xu, Yuchuan Luo, Kai Huang	To bridge the gap, in this paper, we propose a more comprehensive privacy-preserving truth discovery protocol that can simultaneously protect the privacy of participants and truth results.
130	Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas	Teng Wang, Jun Zhao, Han Yu, Jinyan Liu, Xinyu Yang, Xuebin Ren, Shuyu Shi	In this paper, we report a first-of-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas.
131	Privacy Preserving Approximate K-means Clustering	Chandan Biswas, Debasis Ganguly, Dwaipayan Roy, Ujjwal Bhattacharya	To prevent this, we propose to encode the input data in such a way that, firstly, it should be difficult to decode it back to the true data, and secondly, the computational results obtained with the encoded data should not be substantially different from those obtained with the true data.
132	Practical Access Pattern Privacy by Combining PIR and Oblivious Shuffle	Zhilin Zhang, Ke Wang, Weipeng Lin, Ada Wai-Chee Fu, Raymond Chi-Wing Wong	To help eliminate this void, we introduce “access pattern unlinkability” that separates access pattern privacy into short-term privacy at individual query level and long-term privacy at query distribution level.
133	A Hybrid Retrieval-Generation Neural Conversation Model	Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu	In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods.
134	A Latent-Constrained Variational Neural Dialogue Model for Information-Rich Responses	Yanan Zheng, Yan Wang, Lijie Wen, Jianmin Wang	To address it, we initially propose the Latent-Constrained Variational Neural Dialogue Model (LC-VNDM). It follows variational neural dialogue framework, with an utterance encoder, a context encoder and a response decoder hierarchically organized.
135	Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning	Xinyu Duan, Yating Zhang, Lin Yuan, Xin Zhou, Xiaozhong Liu, Tianyi Wang, Ruocheng Wang, Qiong Zhang, Changlong Sun, Fei Wu	In this work, we propose an innovative end-to-end model to address this problem.
136	ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents	Ali Ahmadvand, Harshita Sahijwani, Jason Ingyu Choi, Eugene Agichtein	To complement our model, we propose a simple and effective method for generating synthetic training data, to augment the typically limited amounts of labeled training data, using commonly available knowledge bases as to generate additional labeled utterances.
137	An Interactive Mechanism to Improve Question Answering Systems via Feedback	Xinbo Zhang, Lei Zou, Sen Hu	To provide better interactivity and online performance, we design a holistic graph mining algorithm (HWspan) to automatically refine the query graph.
138	Attentive History Selection for Conversational Question Answering	Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W. Bruce Croft, Mohit Iyyer	In this work, we propose a novel solution for ConvQA that involves three aspects.
139	Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction	Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu	This article addresses this problem by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions.
140	Commonsense Properties from Query Logs and Question Answering Forums	Julien Romero, Simon Razniewski, Koninika Pal, Jeff Z. Pan, Archit Sakhadeo, Gerhard Weikum	This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources.
141	Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms	Avikalp Srivastava, Hsin-Wen Liu, Sumio Fujita	With the increasingly multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images.
142	Message Passing for Complex Question Answering over Knowledge Graphs	Svitlana Vakulenko, Javier David Fernandez Garcia, Axel Polleres, Maarten de Rijke, Michael Cochez	We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers.
143	BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer	Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang	To address these limitations, we proposed a sequential recommendation model called BERT4Rec, which employs the deep bidirectional self-attention to model user behavior sequences.
144	Adaptive Feature Sampling for Recommendation with Missing Content Feature Values	Shaoyun Shi, Min Zhang, Xinxing Yu, Yongfeng Zhang, Bin Hao, Yiqun Liu, Shaoping Ma	In this work, we propose a new adaptive “Feature Sampling” strategy to help train different models to fit distinct scenarios, no matter for cold-start or missing feature value cases.
145	A Dynamic Co-attention Network for Session-based Recommendation	Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke	We propose a DCN-SR. DCN-SR applies a co-attention network to capture the dynamic interactions between the user’s long- and short-term interaction behavior and generates co-dependent representations of the user’s long- and short-term interests.
146	Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation	Di You, Nguyen Vo, Kyumin Lee, Qiang LIU	To overcome these problems and complement existing methods against fake news, in this paper we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook.
147	A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists	Yun He, Jianling Wang, Wei Niu, James Caverlee	Hence, in this paper, we propose a novel user-generated list recommendation model called AttList.
148	HAES: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity	Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang	To address these challenges, we introduce a new model called HAES, a H ybrid A pproach for movie recommendation with E lastic S erendipity, to recommend serendipitous movies.
149	DBRec: Dual-Bridging Recommendation via Discovering Latent Groups	Jingwei Ma, Jiahui Wen, Mingyang Zhong, Liangchen Liu, Chaojie Li, Weitong Chen, Yin Yang, Hongkui Tu, Xue Li	To address this problem, we propose a novel dual-bridging recommendation model (DBRec).
150	Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation	Wang-Cheng Kang, Julian McAuley	In this paper, we seek to investigate these questions via proposing a candidate generation and re-ranking based framework (CIGAR), which first learns a preference-preserving binary embedding for building a hash table to retrieve candidates, and then learns to re-rank the candidates using real-valued ranking models with a candidate-oriented objective.
151	DTCDR: A Framework for Dual-Target Cross-Domain Recommendation	Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, Xiaolin Zheng	To this end, in this paper, we propose a new framework, DTCDR, for Dual-Target Cross-Domain Recommendation.
152	Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling	Kyeongpil Kang, Junwoo Park, Wooyoung Kim, Hojung Choe, Jaegul Choo	In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation.
153	A Spatio-temporal Recommender System for On-demand Cinemas	Taofeng Xue, Beihong Jin, Beibei Li, Weiqing Wang, Qi Zhang, Sihua Tian	In this paper, we propose a novel spatio-temporal approach called Pegasus.
154	Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users	SeongKu Kang, Junyoung Hwang, Dongha Lee, Hwanjo Yu	In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available.
155	Leveraging Ratings and Reviews with Gating Mechanism for Recommendation	Haifeng Xia, Zengmao Wang, Bo Du, Lefei Zhang, Shuai Chen, Gang Chun	In this paper, we propose a hybrid deep collaborative filtering model that jointly learns latent representations from ratings and reviews.
156	Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence	Yin Zhang, James Caverlee	In this paper, we build thefirst visual influence-aware fashion recommender (FIRN) with leveraging fashion bloggers and their dynamic visual posts.
157	What Can History Tell Us?	Ke Sun, Tieyun Qian, Hongzhi Yin, Tong Chen, Yiqi Chen, Ling Chen	In light of this, we propose a novel deep learning based sequential recommender framework for session-based recommendation, which takes Nonlocal Neural Network and Recurrent Neural Network as the main building blocks.
158	Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning	Junqi Zhang, Jiaxin Mao, Yiqun Liu, Ruizhe Zhang, Min Zhang, Shaoping Ma, Jun Xu, Qi Tian	In this paper, we propose a novel framework which aims to improve context-aware listwise ranking performance by optimizing online evaluation metrics.
159	A Multi-Scale Temporal Feature Aggregation Convolutional Neural Network for Portfolio Management	Si Shi, Jianjun Li, Guohui Li, Peng Pan	In this paper, inspired by the Inception network that has achieved great success in computer vision and can extract multi-scale features simultaneously, we propose a novel Ensemble of Identical Independent Inception (EI$^3$) convolutional neural network, with the objective of addressing the limitation of existing reinforcement learning based portfolio management methods.
160	Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning	Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke	In this paper, we propose a graph convolutional reinforcement learning model for MCP, named Combined Order-free Medicine Prediction Network (CompNet), that addresses the issues listed above.
161	Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding	Junwei Lu, Chaoqi Yang, Xiaofeng Gao, Liubin Wang, Changcheng Li, Guihai Chen	To tackle these two challenges in the real-time bidding scenario, we propose ClusterA3C, a novel Advantage Asynchronous Actor-Critic (A3C) variant integrated with a sequential information extraction scheme and a clustering based state aggregation scheme.
162	Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System	Ye Liu, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu	In order to eliminate the effect of ill-formed questions, we approach the question refinement task and propose a unified model, QREFINE, to refine the ill-formed questions to well-formed question.
163	Analyzing the Effects of Document’s Opinion and Credibility on Search Behaviors and Belief Dynamics	Suppanut Pothirattanachaikul, Takehiro Yamamoto, Yusuke Yamamoto, Masatoshi Yoshikawa	The results revealed that (i) the participants spent more effort searching by issuing more queries, when belief-inconsistent documents were presented; (ii) the documents’ opinion and credibility affected their belief dynamics, (i.e., how their beliefs changed after the search task); and (iii) their belief dynamics and search efforts had few relationships.
164	Identifying Facet Mismatches In Search Via Micrographs	Sriram Srinivasan, Nikhil S. Rao, Karthik Subbian, Lise Getoor	In this paper, we use the connections that exist between products and query to identify a special kind of structure we refer to as a micrograph.
165	GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine	Minjia Zhang, Yuxiong He	This paper presents GRIP, an approximate nearest neighbor (ANN) search algorithm for building vector search engine which makes heavy use of the algorithm.
166	Improving Web Image Search with Contextual Information	Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Qingyao Ai, Yufei Huang, Min Zhang, Shaoping Ma	We propose CARM, a context-aware re-ranking model, a neural network-based framework to re-rank web image search results for a query based on previous interaction behavior in the search session in which the query was submitted.
167	Dynamic Bayesian Metric Learning for Personalized Product Search	Teng Xiao, Jiaxin Ren, Zaiqiao Meng, Huan Sun, Shangsong Liang	In this paper, we study the problem of personalized product search under streaming scenarios.
168	Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor	Jingyi Wang, Qiang Liu, Zhaocheng Liu, Shu Wu	In this paper, we propose a CNN & Attention-based Sequential Feature Extractor (CASFE) module to capture the possible features of user behaviors at different time intervals.
169	Locally Slope-based Dynamic Time Warping for Time Series Classification	Jidong Yuan, Qianhong Lin, Wei Zhang, Zhihai Wang	To solve this problem, we propose a novel weighted DTW based on local slope feature (LSDTW), which enhances DTW by taking regional information into consideration.
170	HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling	Yi Cao, Weifeng Zhang, Bo Song, Congfu Xu	In order to combine the advantages of CNN and ATT, we propose a convolutional attention network (CAN), which merges the structure of CNN and ATT into a single neural network and can serve as a new basic module in complex neural networks.
171	Automatic Sequential Pattern Mining in Data Streams	Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai	In this paper, we propose a streaming algorithm, namely StreamScope, that is designed to find intuitive patterns efficiently from event streams evolving over time.
172	Efficient Sequential and Parallel Algorithms for Estimating Higher Order Spectra	Zigeng Wang, Abdullah-Al Mamun, Xingyu Cai, Nalini Ravishanker, Sanguthevar Rajasekaran	In this paper, we present a package of generic sequential and parallel algorithms for computationally and memory efficient HOS estimations which can be employed on any parallel machine or platform.
173	A Modular Adversarial Approach to Social Recommendation	Adit Krishnan, Hari Cheruvu, Cheng Tao, Hari Sundaram	This paper proposes a novel framework to incorporate social regularization for item recommendation.
174	Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities	Xiaobao Wang, Di Jin, Mengquan Liu, Dongxiao He, Katarzyna Musial, Jianwu Dang	In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities.
175	Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization	Hsu-Chao Lai, Hong-Han Shuai, De-Nian Yang, Jiun-Long Huang, Wang-Chien Lee, Philip S. Yu	In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions.
176	Tracking Top-k Influential Users with Relative Errors	Yu Yang, Zhefeng Wang, Tianyuan Jin, Jian Pei, Enhong Chen	In this paper, we tackle the problem of tracking top-k influential individuals in a dynamic social network.
177	NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network	Mohammad Raihanul Islam, Sathappan Muthiah, Naren Ramakrishnan	To fill this gap, we propose a social network augmented neural network model named NActSeer which takes the neighbors’ actions into account in addition to the user’s history.
178	In2Rec: Influence-based Interpretable Recommendation	Huafeng Liu, Jingxuan Wen, Liping Jing, Jian Yu, Xiangliang Zhang, Min Zhang	A learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to maximum a posteriori estimation for In2Rec.
179	Accounting for Temporal Dynamics in Document Streams	Zhendong Chu, Renqin Cai, Hongning Wang	In this paper, we explicitly model mutual influence among topics over time, with the purpose to better understand how events emerge, fade and inherit.
180	How Does BERT Answer Questions?: A Layer-Wise Analysis of Transformer Representations	Betty van Aken, Benjamin Winter, Alexander L?ser, Felix A. Gers	In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT’s hidden states.
181	Patterns of Search Result Examination: Query to First Action	Mustafa Abualsaud, Mark D. Smucker	To determine key factors that affect a user’s behavior with search results, we conducted a controlled eye-tracking study of users completing search tasks using both desktop and mobile devices.
182	A Dynamic Product-aware Learning Model for E-commerce Query Intent Understanding	Jiashu Zhao, Hongshen Chen, Dawei Yin	In this paper, we aim to map the queries into the predefined tens of thousands of fine-grained categories extracted from the product descriptions.
183	Scalable Causal Graph Learning through a Deep Neural Network	Chenxiao Xu, Hao Huang, Shinjae Yoo	This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation.
184	Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection	Babak Hosseini, Barbara Hammer	In this paper, we propose a novel interpretable multiple-kernel prototype learning (IMKPL) to construct highly interpretable prototypes in the feature space, which are also efficient for the discriminative representation of the data.
185	BePT: A Behavior-based Process Translator for Interpreting and Understanding Process Models	Chen Qian, Lijie Wen, Akhil Kumar	In this paper, we propose a novel process translator named BePT (Behavior-based Process Translator) based on the encoder-decoder paradigm, encoding a process model into a middle representation and decoding the representation into NL descriptions.
186	Towards Effective and Interpretable Person-Job Fitting	Ran Le, Wenpeng Hu, Yang Song, Tao Zhang, Dongyan Zhao, Rui Yan	To this end, we propose an Interpretable Person-Job Fit (IPJF) model, which 1) models the Person-Job Fit problem from the perspectives/intentions of employer and job seeker in a multi-tasks optimization fashion to interpretively formulate the Person-Job Fit process; 2) leverages deep interactive representation learning to automatically learn the interdependence between a resume and job requirements without relying on a clear list of job seeker’s abilities, and deploys the optimizing problem as a learning to rank problem.
187	Leveraging Graph Neighborhoods for Efficient Inference	Melisachew Wudage Chekol, Heiner Stuckenschmidt	In this study, we aim to tackle this challenging problem.
188	STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints	Jingyue Gao, Yuanduo He, Yasha Wang, Xiting Wang, Jiangtao Wang, Guangju Peng, Xu Chu	In this paper, we propose a novel Spatio-Temporal Taxonomy-Aware Recommendation model (STAR), to recommend tags for citizen complaints by jointly incorporating spatio-temporal information of complaints and the taxonomy of candidate tags.
189	CoLight: Learning Network-level Cooperation for Traffic Signal Control	Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li	To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.
190	Learning to Effectively Estimate the Travel Time for Fastest Route Recommendation	Ning Wu, Jingyuan Wang, Wayne Xin Zhao, Yang Jin	In this paper, we extend the classic A* algorithm for the FRR task by modeling complex traffic information with neural networks.
191	PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network	Yige Zhang, Weixiong Rao, Kun Zhang, Mingxuan Yuan, Jia Zeng	To address this issue, we propose a deep neural network (DNN)-based position recovery framework, namely PRNet, which can ensemble the power of CNN, sequence model LSTM, and two attention mechanisms to learn local, short- and long-term spatio-temporal dependencies from input MR samples.
192	Active Collaborative Sensing for Energy Breakdown	Yiling Jia, Nipun Batra, Hongning Wang, Kamin Whitehouse	In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown.
193	Forecasting Pavement Performance with a Feature Fusion LSTM-BPNN Model	Yushun Dong, Yingxia Shao, Xiaotong Li, Sili Li, Lei Quan, Wei Zhang, Junping Du	In order to better capture the latent relationship between the cross-sectional and time-series features, we propose a novel feature fusion LSTM-BPNN model.
194	Learning Phase Competition for Traffic Signal Control	Guanjie Zheng, Yuanhao Xiong, Xinshi Zang, Jie Feng, Hua Wei, Huichu Zhang, Yong Li, Kai Xu, Zhenhui Li	In this paper, we propose a novel design called FRAP, which is based on the intuitive principle of phase competition in traffic signal control: when two traffic signals conflict, priority should be given to one with larger traffic movement (i.e., higher demand).
195	Path Travel Time Estimation using Attribute-related Hybrid Trajectories Network	Xi Lin, Yequan Wang, Xiaokui Xiao, Zengxiang Li, Sourav S. Bhowmick	Motivated by these and the recent successes of neural network models, we propose Attribute-related Hybrid Trajectories Network~(AtHy-TNet), a neural model that effectively utilizes the attribute correlations, as well as the spatial and temporal relationships across hybrid trajectory data.
196	CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms	Jiarui Jin, Ming Zhou, Weinan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, Guobin Wu, Jieping Ye	We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms.
197	Unsupervised Representation Learning of Spatial Data via Multimodal Embedding	Porter Jenkins, Ahmad Farag, Suhang Wang, Zhenhui Li	In this paper, we study the Learning an Embedding Space for Regions (LESR) problem, wherein we aim to produce vector representations of discrete regions.
198	Rating Mechanisms for Sustainability of Crowdsourcing Platforms	Chenxi Qiu, Anna Squicciarini, Sarah Rajtmajer	In this paper, we introduce rating mechanisms to evaluate requesters’ behavior, such that the health and sustainability of crowdsourcing platform can be improved.
199	Exploring The Interaction Effects for Temporal Spatial Behavior Prediction	Huan Yang, Tianyuan Liu, Yuqing Sun, Elisa Bertino	In this paper, we adopt a joint embedding (JointE) model to learn the representations of user, location, and users’ action in the same latent space.
200	Social Cards Probably Provide For Better Understanding Of Web Archive Collections	Shawn M. Jones, Michele C. Weigle, Michael L. Nelson	We want to help users answer the question of “What does the underlying collection contain?” But which surrogate should we use? With Mechanical Turk participants, we evaluate six different surrogate types against each other. We find that the type of surrogate does not influence the time to complete the task we presented the participants.
201	Learning from Dynamic User Interaction Graphs to Forecast Diverse Social Behavior	Prasha Shrestha, Suraj Maharjan, Dustin Arendt, Svitlana Volkova	To address these limitations, we propose (1) a novel task — forecasting user interactions over dynamic social graphs, and (2) a novel deep learning, multi-task, node-aware attention model that focuses on forecasting social interactions, going beyond recently emerged approaches for learning dynamic graph embeddings.
202	Understanding Default Behavior in Online Lending	Yang Yang, Yuhong Xu, Chunping Wang, Yizhou Sun, Fei Wu, Yueting Zhuang, Ming Gu	In this paper, we study default prediction in online lending by using social behavior.

TABLE 2: CIKM 2019 Short/Applied Papers

	Title	Authors	Highlight
1	Interpretable MTL from Heterogeneous Domains using Boosted Tree	Ya-Lin Zhang, Longfei Li	In this paper, following the philosophy of boosted tree, we proposed a two-stage method.
2	Machine Reading Comprehension: Matching and Orders	Ao Liu, Lizhen Qu, Junyu Lu, Chenbin Zhang, Zenglin Xu	In this paper, we study the machine reading comprehension of temporal order in text.
3	Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder	Yufei Tian, Jianfei Yu, Jing Jiang	In this paper, we study abstractive review summarization.
4	Datalog Reasoning over Compressed RDF Knowledge Bases	Pan Hu, Jacopo Urbani, Boris Motik, Ian Horrocks	We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing.
5	An Explainable Deep Fusion Network for Affect Recognition Using Physiological Signals	Jionghao Lin, Shirui Pan, Cheng Siong Lee, Sharon Oviatt	In this paper, we propose a deep learning model to process multimodal-multisensory bio-signals for affect recognition.
6	MarlRank: Multi-agent Reinforced Learning to Rank	Shihao Zou, Zhonghua Li, Mohammad Akbari, Jun Wang, Peng Zhang	To mitigate this problem, in this paper, we propose a multi-agent reinforced ranking model, named MarlRank.
7	LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning	Diandian Gu, Ziniu Hu, Shangchen Du, Yun Ma	To address the issue, in this paper, we propose LinkRadar, a data-driven approach to assisting the analysis of inter-app page links.
8	NAD: Neural Network Aided Design for Textile Pattern Generation	Zhifei Pang, Sai Wu, Dongxiang Zhang, Yunjun Gao, Gang Chen	In this paper, we present our NAD system which can automatically produce high-quality textile patterns for printing industry.
9	Feature Selection for Facebook Feed Ranking System via a Group-Sparsity-Regularized Training Algorithm	Xiuyan Ni, Yang Yu, Peng Wu, Youlin Li, Shaoliang Nie, Qichao Que, Chao Chen	In this paper, we propose a novel neural-network-suitable feature selection algorithm, which selects important features from the input layer during training.
10	Fine-Grained Geolocalization of User-Generated Short Text based on Weight Probability Model	Congjie Gao, Yongjnu Li, Jiaqi Yang	To solve these problems, we propose a fine-grained geolocalization method based on a weight probability model (FGST-WP).
11	A Compare-Aggregate Model with Latent Clustering for Answer Selection	Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung	In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing.
12	Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding	Pei-Chi Wang, Cheng-Te Li	In this work, by representing criminal and terrorism activities as a heterogeneous network, we propose a novel unsupervised method, Outlier Spotting with behavior-aware Network Embedding (OSNE), to identify terrorists among potential criminals.
13	Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy	Jun Wu, Jingrui He	To address the above problems, in this paper, we propose a novel MAnifold-RegularIzed Network Embedding (MARINE) algorithm inspired by minimizing the information discrepancy in a Reproducing Kernel Hilbert Space via Maximum Mean Discrepancy.
14	Tensor Decomposition-based Node Embedding	Shah Muhammad Hamdi, Soukaina Filali Boubrahimi, Rafal Angryk	In this paper, we present Tensor Decomposition-based Node Embedding (TDNE), a novel model for learning node representations for arbitrary types of graphs: undirected, directed, and/or weighted.
15	Geometric Estimation of Specificity within Embedding Spaces	Negar Arabzadeh, Fattaneh Zarrinkalam, Jelena Jovanovic, Ebrahim Bagheri	In this work, we explore how neural embeddings can be used to define corpus-independent specificity metrics.
16	Similarity-Aware Network Embedding with Self-Paced Learning	Chao Huang, Baoxu Shi, Xuchao Zhang, Xian Wu, Nitesh V. Chawla	In this paper, we propose a new method named SANE, short for Similarity-Aware Network Embedding, to learn node representations by explicitly considering different similarity degrees between connected nodes in a network.
17	Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding	Hansheng Xue, Jiajie Peng, Jiying Li, Xuequn Shang	To address this critical problem, we propose a novel node embedding, called DeepMNE, for multi-network integration using a deep semi-supervised autoencoder.
18	Query-Specific Knowledge Summarization with Entity Evolutionary Networks	Carl Yang, Lingrui Gan, Zongyi Wang, Jiaming Shen, Jinfeng Xiao, Jiawei Han	In this work, to facilitate such a novel insightful search system, we propose SetEvolve, which is a unified framework based on nonparanomal graphical models for evolutionary network construction from large text corpora.
19	Real-time Edge Repartitioning for Dynamic Graph	He Li, Hang Yuan, Jianbin Huang	In this paper, we propose a real-time edge repartitioning algorithm for dynamic graph, which reduces the vertex replicas by reassigning edges near the new edge.
20	DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting	Siteng Huang, Donglin Wang, Xuehan Wu, Ao Tang	In this paper, we propose a dual self-attention network (DSANet) for highly efficient multivariate time series forecasting, especially for dynamic-period or nonperiodic series.
21	Time Series Prediction with Interpretable Data Reconstruction	Qiangxing Tian, Jinxin Liu, Donglin Wang, Ao Tang	In this paper, we propose a novel predictor which integrates the sequence to sequence (seq2seq) model based on long short-term memory units (LSTM) with interpretable data reconstruction, where the learned hidden state is taken as a bridge.
22	Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks	Zhining Liu, Dawei Zhou, Jingrui He	To address this question, we propose a generic graph attention neural mechanism named STANE, which guides the context sampling process to focus on the crucial part of the data.
23	Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity	Chao Huang, Xian Wu, Xuchao Zhang, Suwen Lin, Nitesh V. Chawla	To address these challenges, we propose a prototype embedding framework-Deep Prototypical Networks (DPN), which leverages a main embedding space to capture the discrepancies of difference time series classes for alleviating data scarcity.
24	Knowledge-aware Textual Entailment with Graph Attention Network	Daoyuan Chen, Yaliang Li, Min Yang, Hai-Tao Zheng, Ying Shen	In the paper, we propose a Knowledge-Context Interactive Textual Entailment Network (KCI-TEN) that learns graph level sentence representations by harnessing external knowledge graph with graph attention network.
25	Fast Approximations of Betweenness Centrality with Graph Neural Networks	Sunil Kumar Maurya, Xin Liu, Tsuyoshi Murata	In this paper, we present a Graph Neural Network(GNN) based inductive framework which uses constrained message passing of node features to approximate betweenness centrality.
26	Neighborhood Interaction Attention Network for Link Prediction	Zhitao Wang, Yu Lei, Wenjie Li	In this paper, we propose a novel link prediction neural model named Neighborhood Interaction Attention Network (NIAN), which is able to automatically learn comprehensive neighborhood interaction features and predict links in an end-to-end way.
27	Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning	Man Wu, Shirui Pan, Lan Du, Ivor Tsang, Xingquan Zhu, Bo Du	To overcome these limitations, this paper presents a novel long-short distance aggregation networks (\textttLSDAN ) for positive unlabeled (PU) graph learning.
28	Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks	Yiying Yang, Zhongyu Wei, Qin Chen, Libo Wu	We introduce financial news as supplementary information to solve problems of multiple interpretations of same financial event. For the evaluation, we build a new dataset consisting of financial events for thousands of Chinese listed companies from 2013 to 2017.
29	Cross-Domain Recommendation via Preference Propagation GraphNet	Cheng Zhao, Chenliang Li, Cong Fu	In this paper, we propose the Preference Propagation GraphNet (PPGN) to address the above problems.
30	ARP: Aspect-aware Neural Review Rating Prediction	Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang, Xing Xie	In this paper, we propose a neural aspect-aware rating prediction approach for Chinese reviews.
31	CosRec: 2D Convolutional Neural Networks for Sequential Recommendation	An Yan, Shuo Cheng, Wang-Cheng Kang, Mengting Wan, Julian McAuley	In this paper, we argue that modeling pairwise relationships directly leads to an efficient representation of sequential features and captures complex item correlations.
32	Data Poisoning Attacks on Cross-domain Recommendation	Huiyuan Chen, Jing Li	Here we study the vulnerability of cross-domain recommendation under data poisoning attacks. We show that data poisoning attacks can be formulated as a bilevel optimization problem.
33	Session-based Recommendation with Hierarchical Memory Networks	Bo Song, Yi Cao, Weifeng Zhang, Congfu Xu	In this paper, we aim to leverage n-gram features and model users’ feature-level preferences in an explicit and effective manner.
34	Correcting for Recency Bias in Job Recommendation	Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, W. Bruce Croft	In this study, we characterize this temporal influence as a recency bias, and present an analysis in the domain of job recommendation.
35	Motif Enhanced Recommendation over Heterogeneous Information Network	Huan Zhao, Yingqi Zhou, Yangqiu Song, Dik Lun Lee	In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations.
36	GPU-Accelerated Decoding of Integer Lists	Antonio Mallia, Michal Siedlaczek, Torsten Suel, Mohamed Zahran	In this paper, we describe and implement two encoding schemes for index decompression on GPU architectures.
37	Synergizing Local and Global Models for Matrix Approximation	Chao Chen, Hao Zhang, Dongsheng Li, Junchi Yan, Xiaokang Yang	This paper proposes a new ensemble learning framework, in which the local models and global models are synergetically updated from each other.
38	Deep Colorization by Variation	Zineng Tang	We propose an adversarial learning based model for image colorization in which we elaborately adapt image translation mechanism that are optimized according to the task.
39	Fast Random Forest Algorithm via Incremental Upper Bound	Yasuhiro Fujiwara, Yasutoshi Ida, Sekitoshi Kanai, Atsutoshi Kumagai, Junya Arai, Naonori Ueda	This paper proposes F-forest, an efficient variant of random forest.
40	Convolution-Consistent Collective Matrix Completion	Xu Liu, Jingrui He, Sam Duddy, Liz O’Sullivan	To address this problem, in this paper, we propose a new collective matrix completion framework, named C4, which uses the graph spectral filters to capture the non-Euclidean cross-matrix information.
41	Faster Algorithms for k-Regret Minimizing Sets via Monotonicity and Sampling	Qi Dong, Jiping Zheng	In this paper, we propose a faster algorithm SAMPGREED for k-RMS queries by utilizing the monotonicity of the regret ratio function with sampling techniques.
42	Towards Stochastic Simulations of Relevance Profiles	Kevin Roitero, Andrea Brunello, Juli?n Urbano, Stefano Mizzaro	In this paper we address this limitation: we present an approach that exploits an evolutionary algorithm and, given a metric score, creates a simulated relevance profile (i.e., a ranked list of relevance values) that produces that score.
43	SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks	Yuening Li, Xiao Huang, Jundong Li, Mengnan Du, Na Zou	In this paper, we propose a spectral convolution and deconvolution based framework – SpecAE, to project the attributed network into a tailored space to detect global and community anomalies.
44	On Continuously Matching of Evolving Graph Patterns	Qianzhen Zhang, Deke Guo, Xiang Zhao, Aibo Guo	Specially, we propose a concise representation \Index of partial matching solutions, and its execution model allows fast incremental maintenance.
45	Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling	Won-Seok Hwang, Jeong-Han Yun, Jonguk Kim, Hyoung Chun Kim	We proposetime-series aware precision andrecall, which are appropriate for evaluating anomaly detection methods in time-series data.
46	Additive Explanations for Anomalies Detected from Multivariate Temporal Data	Ioana Giurgiu, Anika Schumann	In this paper, we extend SHAP — a unified framework for providing additive explanations, previously applied for supervised models — with influence weighting, in order to explain anomalies detected from multivariate time series with a GRU-based autoencoder.
47	ED2: A Case for Active Learning in Error Detection	Felix Neutatz, Mohammad Mahdavi, Ziawasch Abedjan	To this end, we propose a multi-classifier approach with two-stage sampling for active learning.
48	Multi-scale Trajectory Clustering to Identify Corridors in Mobile Networks	Li Li, Sarah Erfani, Chien Aun Chan, Christopher Leckie	To identify the underlying geographical corridors of users in mobile networks, we propose a hierarchical multi-scale trajectory clustering algorithm for corridor identification by analyzing the non-homogeneity of the spatial distribution of cell towers and users’ movements.
49	Multi-view Moments Embedding Network for 3D Shape Recognition	Jun Xiao, Yuanxing Zhang, Pengyu Zhao, Kecheng Xiao, Kaigui Bian, Chunli Zhang, Wei Yan	In this paper, we propose a novel Multi-view Moments Embedding Network(MMEN) for capturing multiple moments information.
50	Active Entity Recognition in Low Resource Settings	Ning Gao, Nikos Karampatziakis, Rahul Potharaju, Silviu Cucerzan	We propose an entity recognition framework that combines active learning and conditional random fields (CRF), and which provides the flexibility to define new entity types as needed by the users.
51	On Novel Object Recognition: A Unified Framework for Discriminability and Adaptability	Kai Li, Martin Renqiang Min, Bing Bai, Yun Fu, Hans Peter Graf	To secure both key factors, we propose a framework which decouples a deep classification model into a feature extraction module and a classification module.
52	Exploiting Multiple Embeddings for Chinese Named Entity Recognition	Canwen Xu, Feiyang Wang, Jialong Han, Chenliang Li	In this paper, we propose a simple yet effective neural framework to derive the character-level embeddings for NER in Chinese text, named ME-CNER.
53	Gate-based Bidirectional Interactive Decoding Network for Scene Text Recognition	Yunze Gao, Yingying Chen, Jinqiao Wang, Hanqing Lu	In this paper, we propose a novel Gate-based Bidirectional Interactive Decoding Network (GBIDN) for scene text recognition.
54	Modeling Long-Range Context for Concurrent Dialogue Acts Recognition	Yue Yu, Siyao Peng, Grace Hui Yang	In this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN) which models the interactions between utterances of long-range context.
55	Labelling for Venue Visit Detection by Matching Wi-Fi Hotspots with Businesses	Denis Shaposhnikov, Anastasia Bezzubtseva, Ekaterina Gladkikh, Alexey Drutsa	In this paper, we present a novel approach to label large quantities of location data as visits based on the following intuition: if a user is connected to a Wi-Fi hotspot of some venue, she is visiting the venue.
56	Heterogeneous Components Fusion Network for Load Forecasting of Charging Stations	Kai Li, Fei Yu, Cheng Feng, Tian Xia	To fill the gap, we present a Heterogeneous Components Fusion Network to model dual components sourced from the planned and the unplanned recharging events independently.
57	Learning Traffic Signal Control from Demonstrations	Yuanhao Xiong, Guanjie Zheng, Kai Xu, Zhenhui Li	Therefore, we propose DemoLight, for the first time, to leverage demonstrations collected from classic methods to accelerate learning.
58	Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction	Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Wei Liu, Zheng Yang	We propose an end-to-end deep learning framework to address the above problems.
59	Collaborative Analysis for Computational Risk in Urban Water Supply Systems	Di Wu, Hao Wang, Razak Seidu	To address this challenge, we propose a data-driven risk analysis method which is low-cost and efficient.
60	Long- and Short-term Preference Learning for Next POI Recommendation	Yuxia Wu, Ke Li, Guoshuai Zhao, Xueming Qian	To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information.
61	Cluster-Based Focused Retrieval	Eilon Sheetrit, Oren Kurland	Inspired by work on cluster-based document retrieval, we present a novel cluster-based focused retrieval method.
62	Cross-modal Image-Text Retrieval with Multitask Learning	Junyu Luo, Ying Shen, Xiang Ao, Zhou Zhao, Min Yang	In this paper, we propose a multi-task learning approach for cross-modal image-text retrieval.
63	A Unified Generation-Retrieval Framework for Image Captioning	Chunpu Xu, Wei Zhao, Min Yang, Xiang Ao, Wangrong Cheng, Jinwen Tian	In this paper, we propose a Unified Generation-Retrieval framework for Image Captioning (UGRIC) by using adversarial learning.
64	A Lossy Compression Method on Positional Index for Efficient and Effective Retrieval	Shuni Gao, Jipeng Liu, Xiaoguang Liu, Gang Wang	In this paper, we propose a lossy method for compressing term position data in the case of utilizing term proximity.
65	Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots	Jia-Chen Gu, Zhen-Hua Ling, Quan Liu	In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task.
66	Analysis of Adaptive Training for Learning to Rank in Information Retrieval	Saar Kuzi, Sahiti Labhishetty, Shubhra Kanti Karmaker Santu, Prasad Pradip Joshi, ChengXiang Zhai	In this paper, we present a Best-Feature Calibration (BFC) strategy for analyzing learning to rank models and use this strategy to examine the benefit of query-level adaptive training.
67	QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation	Panpan Wang, Zhao Li, Yazhou Zhang, Yuexian Hou, Liangzhu Ge	To fill in the gap, we propose an approach, called quantum inspired preference interactive networks (QPIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to interactively learn user preferences.
68	A Study of Context Dependencies in Multi-page Product Search	Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft	In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
69	Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce	Zhenxin Fu, Feng Ji, Wenpeng Hu, Wei Zhou, Dongyan Zhao, Haiqing Chen, Rui Yan	Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse.
70	Neural Review Rating Prediction with User and Product Memory	Zhigang Yuan, Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie	In this paper, we propose a novel framework for review rating prediction with user and product memory.
71	Intent Term Weighting in E-commerce Queries	Saurav Manchanda, Mohit Sharma, George Karypis	In this paper, we leverage the historical query reformulation logs of a large e-retailer (walmart.com) to develop a distant-supervision-based approach to identify the relevant terms that characterize the query’s product intent.
72	Fine-Grained Product Categorization in E-commerce	Hongshen Chen, Jiashu Zhao, Dawei Yin	To address these issues, we proposes a novel Neural Product Categorization model—NPC to identify fine-grained categories from the product content.
73	Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features	Miao Fan, Yeqi Bai, Mingming Sun, Ping Li	In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC.
74	Meta-GNN: On Few-shot Node Classification in Graph Meta-learning	Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Ji Geng	Towards this, we propose a novel graph meta-learning framework — Meta-GNN — to tackle the few-shot node classification problem in graph meta-learning settings.
75	Enriching Pre-trained Language Model with Entity Information for Relation Classification	Shanchan Wu, Yifan He	In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task.
76	Unsupervised Concept Drift Detection with a Discriminative Classifier	?mer G?z?a?ik, Alican B?y?k?akir, Hamed Bonab, Fazli Can	In this study, we present an unsupervised method called D3 which uses a discriminative classifier with a sliding window to detect concept drift by monitoring changes in the feature space.
77	Hybrid Deep Pairwise Classification for Author Name Disambiguation	Kunho Kim, Shaurya Rohatgi, C. Lee Giles	Here, we introduce a hybrid method which takes advantage of both approaches by extracting both structure-aware features and global features.
78	Approximate Definitional Constructs as Lightweight Evidence for Detecting Classes Among Wikipedia Articles	Marius Pasca	A lightweight method applies a few extraction patterns to the task of distinguishing Wikipedia articles that are classes (“Walled garden”, “Garden”) from other articles (“High Hazels Park”).
79	Towards the Gradient Vanishing, Divergence Mismatching and Mode Collapse of Generative Adversarial Nets	Zhaoyu Zhang, Changwei Luo, Jun Yu	To overcome these problems, we propose a novel GAN, which consists of one generator G and two discriminators (D1, D2).
80	Generating Paraphrase with Topic as Prior Knowledge	Yuanxin Liu, Zheng Lin, Fenglin Liu, Qinyun Dai, Weiping Wang	To exploit topic in paraphrase generation, we incorporate topic words into the Seq2Seq framework through a topic-aware input and a topic-biased generation distribution.
81	Sexual Harassment Story Classification and Key Information Identification	Yingchi Liu, Quanzhi Li, Xiaozhong Liu, Qiong Zhang, Luo Si	In this study, we proposed neural network models to extract key elements including harasser, time, location and trigger words.
82	Neural Review Summarization Leveraging User and Product Information	Hui Liu, Xiaojun Wan	In this paper, we explore different ways to leverage the user and product information to help review summarization.
83	Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning	Jiangnan Xia, Chen Wu, Ming Yan	In this paper, we integrate relational knowledge into MRC model for commonsense reasoning.
84	DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis	Song Cheng, Qi Liu, Enhong Chen, Zai Huang, Zhenya Huang, Yiying Chen, Haiping Ma, Guoping Hu	To this end, in this paper, we propose a simple yet surprisingly effective framework to enhance the semantic exploiting process, which we termed Deep Item Response Theory (DIRT).
85	Neural Gender Prediction in Microblogging with Emotion-aware User Representation	Chuhan Wu, Fangzhao Wu, Tao Qi, Junxin Liu, Yongfeng Huang, Xing Xie	In this paper, we propose a neural approach for gender prediction in social media based on both content and emotion of messages posted by users.
86	Health Card Retrieval for Consumer Health Search: An Empirical Investigation of Methods	Jimmy, Guido Zuccon, Bevan Koopman, Gianluca Demartini	In this paper, we focus on difficult queries with self-diagnosis intents.
87	NICE: Neural In-Hospital Cost Estimation from Medical Records	Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie	In this paper, we propose a neural in-hospital cost estimation (NICE) approach to estimate the in-hospital costs of patients from their admission records.
88	Modeling Sentiment Evolution for Social Incidents	Yunjie Wang, Hui Li, Chen Lin	Based on it, we propose to simultaneously model the evolution of background opinion and the sentiment shift using a state space model on the time series of sentiment polarities.
89	Adaptive Feature Redundancy Minimization	Rui Zhang, Hanghang Tong, Yifan Hu	To tackle this problem, we propose a framework regarding adaptive redundancy minimization (ARM) for the feature selection.
90	Finding a Maximum Clique in Dense Graphs via ?2 Statistics	Sourav Dutta, Juho Lauri	In this paper, we propose the ALTHEA heuristic to efficiently extract a maximum clique from a dense graph.
91	On Heavy-user Bias in A/B Testing	Yu Wang, Somit Gupta, Jiannan Lu, Ali Mahmoudzadeh, Sophia Liu	In this paper, we provide theoretical analysis to show that heavy-users can contribute significantly to the bias, and propose a re-sampling estimator for bias adjustment.
92	Adversarial Training of Gradient-Boosted Decision Trees	Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei	In this paper, we generalize adversarial training to gradient-boosted decision trees (GBDTs).
93	Adversarial Structured Neural Network Pruning	Xingyu Cai, Jinfeng Yi, Fan Zhang, Sanguthevar Rajasekaran	In this paper, we employ the idea of adversarial examples to sparsify a CNN.
94	Ontology-Mediated Queries over Probabilistic Data via Probabilistic Logic Programming	Timothy van Bremen, Anton Dries, Jean Christoph Jung	We study ontology-mediated querying over probabilistic data for the case when the ontology is formulated in EL(hdr), an expressive member of the EL family of description logics.
95	Query Embedding Learning for Context-based Social Search	Yi-Chun Chen, Yu-Che Tsai, Cheng-Te Li	Assume each user is associated a set of labels, we propose a novel task, Search by Social Contexts (SSC), in online social networks.
96	Towards More Usable Dataset Search: From Query Characterization to Snippet Generation	Jinchi Chen, Xiaxia Wang, Gong Cheng, Evgeny Kharlamov, Yuzhong Qu	In this ongoing work towards developing a more usable dataset search engine, we characterize real data needs by annotating the semantics of 1,947 queries using a novel fine-grained scheme, to provide implications for enhancing dataset search.
97	Session-based Search Behavior in Naturalistic Settings for Learning-related Tasks	Souvick Ghosh, Chirag Shah	In this research, we investigate the behavioral patterns exhibited in different search sessions as users attempt to complete search tasks of increasing cognitive complexity.
98	Best Co-Located Community Search in Attributed Networks	Jiehuan Luo, Xin Cao, Xike Xie, Qiang Qu	In this paper, we study the problem of searching the \underlineB est \underlineC o-located \underlineC ommunity (\BCC) in attributed networks, which returns a community that satisfies the following properties: i) structural cohesiveness: members in the community are densely connected, ii) spatial co-location: members are close to each other, and iii) quality optimality: the community has the best quality in terms of given attributes.
99	Caching Scores for Faster Query Processing with Dynamic Pruning in Search Engines	Erman Yafay, Ismail Sengor Altingovde	We propose to use a score cache, which stores the score of the k-th result of a query, to accelerate top-k query processing with dynamic pruning methods (i.e., WAND and BMW).
100	Investigating the Learning Process in Job Search: A Longitudinal Study	Jiaxin Mao, Damiano Spina, Sargol Sadeghi, Falk Scholer, Mark Sanderson	We investigated the learning process in search by conducting a log-based study involving registered job seekers of a commercial job search engine.
101	Set Reconciliation with Cuckoo Filters	Lailong Luo, Deke Guo, Ori Rottenstreich, Richard T.B Ma, Xueshan Luo	In this paper, we present a novel reconciliation method based on Cuckoo filter (CF).
102	Shared-Nothing Distributed Enumeration of 2-Plexes	Alessio Conte, Donatella Firmani, Maurizio Patrignani, Riccardo Torlone	We present a novel approach for the detection of 2-plexes, a popular relaxation of cliques used for modeling network communities.
103	Estimating the Number of Distinct Items in a Database by Sampling	Roel Apfelbaum	In this paper, we propose an estimation method for this problem, which is especially suitable for cases where the sample is much smaller than the entire set, and the number of repetitions of each item is relatively small.
104	Cost-effective Resource Provisioning for Spark Workloads	Yuxing Chen, Jiaheng Lu, Chen Chen, Mohammad Hoque, Sasu Tarkoma	In this paper, we study the challenge of determining the proper parameter values that meet the performance requirements of workloads while minimizing both resource cost and resource utilization time.
105	A Sampling-Based System for Approximate Big Data Analysis on Computing Clusters	Salman Salloum, Yinxu Wu, Joshua Zhexue Huang	In this paper, we present a prototype RSP-based system and demonstrate its advantages.
106	TianGong-ST: A New Dataset with Large-scale Refined Real-world Web Search Sessions	Jia Chen, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma	To tackle this obstacle, we present a new dataset that contains 147,155 refined web search sessions with both click-based and human-annotated relevance labels.
107	Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance	Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Jianmin Wu, Yinghui Xu, Rong Jin	In this paper, we propose to discover Virtual ID from user click behavior to improve visual search relevance at Alibaba.
108	Autor3: Automated Real-time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising	Yusi Zhang, Zhi Yang, Liang Wang, Li He	In this paper, we propose an automatic adaptive auction system called Autor 3.
109	Cross-domain Attention Network with Wasserstein Regularizers for E-commerce Search	Minghui Qiu, Bo Wang, Cen Chen, Xiaoyi Zeng, Jun Huang, Deng Cai, Jingren Zhou, Forrest Sheng Bao	Considering each platform as a domain, we propose a cross-domain learning approach to help the task on data-deficient platforms by leveraging the data from data-abundant platforms.
110	Conceptualize and Infer User Needs in E-commerce	Xusheng Luo, Yonghua Yang, Kenny Qili Zhu, Yu Gong, Keping Yang	Backed by such an e-commerce knowledge graph, we propose a supervised learning algorithm to conceptualize user needs from their transaction history as “concept” nodes in the graph and infer those concepts for each user through a deep attentive model.
111	Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds	Dagui Chen, Junqi Jin, Weinan Zhang, Fei Pan, Lvyin Niu, Chuan Yu, Jun Wang, Han Li, Jian Xu, Kun Gai	Considering this mechanism, we propose a novel perspective that advertisers can strategically bid through the advertising platform to optimize their recommended organic traffic.
112	System Deterioration Detection and Root Cause Learning on Time Series Graphs	Hao Huang, Shinjae Yoo, Yunwen Xu	To address these problems, we propose Robust Failure Detection and Diagnosis (RoFaD).
113	A Dynamic Default Prediction Framework for Networked-guarantee Loans	Dawei Cheng, Yiyi Zhang, Fangzhou Yang, Yi Tu, Zhibin Niu, Liqing Zhang	We propose a dynamic default prediction framework (DDPF), which preserves temporal network structures and loan behavior sequences in an end-to-end model.
114	Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native	Morelle Arian, Eliran Abutbul, Michal Aharon, Yair Koren, Oren Somekh, Rotem Stram	In this work, we present a framework that simplifies complex interactions between users and other entities in a bipartite graph.
115	Large-Scale Visual Search with Binary Distributed Graph at Alibaba	Kang Zhao, Pan Pan, Yun Zheng, Yanhao Zhang, Changxu Wang, Yingya Zhang, Yinghui Xu, Rong Jin	In this paper, we propose a novel algorithm called Binary Distributed Graph to solve this problem.
116	Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing	Ziqi Liu, Dong Wang, Qianyu Yu, Zhiqiang Zhang, Yue Shen, Jian Ma, Wenliang Zhong, Jinjie Gu, Jun Zhou, Shuang Yang, Yuan Qi	In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.
117	Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU	Yu Zhu, Yu Gong, Qingwen Liu, Yingcai Ma, Wenwu Ou, Junxiong Zhu, Beidou Wang, Ziyu Guan, Deng Cai	A novel query-based interactive recommender system is proposed in this paper, where personalized questions are accurately generated from millions of automatically constructed questions in Step 1, and the recommendation is ensured to be closely-related to users’ feedback in Step 2.
118	Learning Adaptive Display Exposure for Real-Time Advertising	Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Weinan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai	In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
119	What You Look Matters?: Offline Evaluation of Advertising Creatives for Cold-start Problem	Zhichen Zhao, Lei Li, Bowen Zhang, Meng Wang, Yuning Jiang, Li Xu, Fengkun Wang, Weiying Ma	Specifically, we propose Pre Evaluation Ad Creation Model (PEAC), a novel method to evaluate creatives even before they were shown in the online ads system.
120	Multi-Interest Network with Dynamic Routing for Recommendation at Tmall	Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, Dik Lun Lee	In this paper, we approach the learning of user representations from a different view, by representing a user with multiple representation vectors encoding the different aspects of the user’s interests.
121	Learning to be Relevant: Evolution of a Course Recommendation System	Shivani Rao, Konstantin Salomatin, Gungor Polatkan, Mahesh Joshi, Sneha Chaudhari, Vladislav Tcheprasov, Jeffrey Gee, Deepak Kumar	We present the evolution of a large-scale content recommendation platform for LinkedIn Learning, serving 645M+ LinkedIn users across several different channels (e.g., desktop, mobile).
122	SDM: Sequential Deep Matching Model for Online Large-scale Recommender System	Fuyu Lv, Taiwei Jin, Changlong Yu, Fei Sun, Quan Lin, Keping Yang, Wilfred Ng	In this paper, we propose a new sequential deep matching (SDM) model to capture users’ dynamic preferences by combining short-term sessions and long-term behaviors.
123	Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching	Ming Zhou, Jiarui Jin, Weinan Zhang, Zhiwei Qin, Yan Jiao, Chenxi Wang, Guobin Wu, Yong Yu, Jieping Ye	In this paper, we propose a decentralized execution order-dispatching method based on multi-agent reinforcement learning to address the large-scale order-dispatching problem.
124	MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data	Miao Fan, Jizhou Huang, An Zhuo, Ying Li, Ping Li, Haifeng Wang	In this paper, we introduce our newly launched project “Monopoly” (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals, schools, and metros) with the large-scale urban data we have accumulated via Baidu Maps.
125	CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach	Xiuwen Yi, Zhewen Duan, Ting Li, Tianrui Li, Junbo Zhang, Yu Zheng	For modeling citywide traffic, inspired by the observations of missing patterns and prior knowledge about traffic, we propose a neural memorization and generalization approach to infer the missing speed and volume, which mainly consists of a memorization module for speed inference and a generalization module for volume inference.
126	Deep Dynamic Fusion Network for Traffic Accident Forecasting	Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo	To address these challenges, we develop a deep Dynamic Fusion Network framework (DFN), to explore the central theme of improving the ability of deep neural network on modeling heterogeneous external factors in a fully dynamic manner for traffic accident forecasting.
127	Matrix Factorization for Spatio-Temporal Neural Networks with Applications to Urban Flow Prediction	Zheyi Pan, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang, Yu Zheng	To tackle these challenges, we propose a novel framework that employs matrix factorization for spatio-temporal neural networks (MF-STN), capable of enhancing the state-of-the-art deep ST models.
128	Semantically Driven Auto-completion	Konstantine Arkoudas, Mohamed Yahya	We describe the auto-complete problem as it arises in this setting, the novel algorithms that we use to solve it, and report on the quality of the results and the efficiency of our approach.
129	Spam Review Detection with Graph Convolutional Networks	Ao Li, Zhou Qin, Runshi Liu, Yiqun Yang, Dong Li	In this paper, we present our technical solutions to address these challenges.
130	Industry Specific Word Embedding and its Application in Log Classification	Elham Khabiri, Wesley M. Gifford, Bhanukiran Vinzamuri, Dhaval Patel, Pietro Mazzoleni	To address the problem, this paper proposes a semi-supervised learning framework to create document corpus and embedding starting from an industry taxonomy, along with a very limited set of relevant positive and negative documents.
131	Document-Level Multi-Aspect Sentiment Classification for Online Reviews of Medical Experts	Tian Shi, Vineeth Rakesh, Suhang Wang, Chandan K. Reddy	In this paper, we systematically investigate the dataset from one of such review platform, namely, ratemds.com, where each review for a doctor comes with an overall rating and ratings of four different aspects.
132	Deep Learning for Blast Furnaces: Skip-Dense Layers Deep Learning Model to Predict the Remaining Time to Close Tap-holes for Blast Furnaces	Keeyoung Kim, Byeongrak Seo, Sang-Hoon Rhee, Seungmoon Lee, Simon S. Woo	In this paper, we use a data-driven deep learning method to more accurately predict the remaining time to close each tap-hole in a blast furnace and develop an AI-enabled automated advisory system to reduce manual human efforts as well as operation cost.
133	Deep Graph Similarity Learning for Brain Data Analysis	Guixiang Ma, Nesreen K. Ahmed, Theodore L. Willke, Dipanjan Sengupta, Michael W. Cole, Nicholas B. Turk-Browne, Philip S. Yu	We propose an end-to-end graph similarity learning framework called Higher-order Siamese GCN for multi-subject fMRI data analysis.
134	How to Find It Better?: Cross-Learning for WeChat Mini Programs	He Li, Zhiqiang Liu, Sheng Xu, Zhiyuan Lin, Xiangqun Chen	In this paper, we propose a Cross-Learning strategy to improve the search experience, where the semantics of queries and Mini Programs are represented not by itself, but by each other.
135	Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning	Denghui Zhang, Junming Liu, Hengshu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong	Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view (the structure of relationships among job titles), (2) semantic view (semantic meaning of job descriptions), (3) job transition balance view (the numbers of bidirectional transitions between two similar-level jobs are close), and (4) job transition duration view (the shorter the average duration of transitions is, the more similar the job titles are).
136	Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices	Jyun-Yu Jiang, Zehan Chao, Andrea L. Bertozzi, Wei Wang, Sean D. Young, Deanna Needell	In this paper, we propose DataCompletion with Diurnal Regularizers (DCDR) and TemporallyHierarchical Attention Network (THAN) to address the fragmented data issue and predict human stress level with recovered sensor data.
137	Fine-Grained Fuel Consumption Prediction	Chenguang Fang, Shaoxu Song, Zhiwei Chen, Acan Gui	In this paper, we propose to predict the fine-grained fuel consumption rate of each engine speed and torque combination, by learning a model from the incomplete and inconsistent observation data.
138	Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native	Michal Aharon, Yohay Kaplan, Rina Levy, Oren Somekh, Ayelet Blanc, Neetai Eshel, Avi Shahar, Assaf Singer, Alex Zlotnik	Therefore, to improve click prediction accuracy, we propose a soft frequency capping (SFC) approach, where the frequency feature is incorporated into the ?ffset model as a user-ad feature and its weight vector is learned via logistic regression as part of offset training.
139	Adversarial Factorization Autoencoder for Look-alike Modeling	Khoa D. Doan, Pranjul Yadav, Chandan K. Reddy	To overcome these limitations, in this paper, we propose a novel Adversarial Factorization Autoencoder that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure.
140	Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring	Hongda Tian, Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang, Fang Chen	To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper.
141	Multi-task based Sales Predictions for Online Promotions	Shen Xin, Martin Ester, Jiajun Bu, Chengwei Yao, Zhao Li, Xun Zhou, Yizhou Ye, Can Wang	Therefore, several models are proposed with part of features that are possibly beneficial to other tasks, which indicates the universal representation for the items needs to be learned across different promotion scenarios.
142	Experimental Study of Multivariate Time Series Forecasting Models	Jiaming Yin, Weixiong Rao, Mingxuan Yuan, Jia Zeng, Kai Zhao, Chenxi Zhang, Jiangfeng Li, Qinpei Zhao	To this end, in this paper, we conduct a systematic evaluation of eight representative forecasting models over eight multivariate time series datasets, and have the following findings: 1) When the datasets exhibit strong periodic patterns, deep learning models perform best. Otherwise on the datasets in a non-periodic manner, the statistical models such as ARIMA perform best.
143	GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games	Jianrong Tao, Linxia Gong, Changjie Fan, Longbiao Chen, Dezhi Ye, Sha Zhao	In this paper, we propose a graph attention recurrent network (GART) based multi-task learning model (GMTL) to fuse information across multiple social-temporal prediction tasks.
144	Learning Compositional, Visual and Relational Representations for CTR Prediction in Sponsored Search	Xiao Yang, Tao Deng, Weihan Tan, Xutian Tao, Junwei Zhang, Shouke Qin, Zongyao Ding	To tackle these problems, in this paper, we propose an approach to improve the accuracy of CTR prediction by learning supplementary representations from three new aspects: the compositional components, the visual appearance and the relational structure of ads.