SIGIR 2020 Main Conference Program
Monday, July 27, 2020
Session 1A (July 27,
9:40-11:40)
Neural IR and Semantic Matching
Chair: Hamed Zamani (Microsoft)
- Training Effective Neural CLIR by Bridging the Translation Gap. Hamed
Bonab,
Sheikh Muhammad Sarwar, and James Allan
- A Quantum Interference Inspired Neural Matching Model for Ad-hoc
Retrieval.
Yongyu Jiang, Peng Zhang, Hui Gao, and Dawei Song
- A Deep Recurrent Survival Model for Unbiased Ranking. Jiarui Jin, Yuchen
Fang, Weinan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang,
Xiaoqiang Zhu, and Kun Gai
- ColBERT: Efficient and Effective Search via Contextualized Late
Interaction
over BERT. Omar Khattab and Matei Zaharia
- Efficient Document Re-Ranking for Transformers by Precomputing Term
Representations. Sean MacAvaney, Franco Maria Nardini, Raffaele Perego,
Nicola Tonellotto, Nazli Goharian, Ophir Frieder
- A Reinforcement Learning Framework for Relevance Feedback, Ali
Montazeralghaem, Hamed Zamani, and James Allan
Session 1B (July 27,
9:40-11:40)
Knowledge and Explainability
Chair: Vanessa Murdock (Amazon)
- Fairness-Aware Explainable Recommendation over Knowledge Graphs. Zuohui
Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge,
Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, and Gerard de Melo
- Attentional Graph Convolutional Networks for Knowledge Concept
Recommendation in MOOCs in a Heterogeneous View. Jibing Gong, Shen Wang,
Jinlong Wang, Hao Peng, Wenzheng Feng, Dan Wang, Yi Zhao, Huanhuan Li,
Jie Tang, and P. Yu
- Sequential Recommendation with Self-attentive Multi-adversarial Network.
Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin
Ding, and Ji-Rong Wen
- MVIN: Learning multiview items for recommendation. Chang-You Tai,
Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, and Lun-Wei Ku
- Make It a CHORUS: Context- and Knowledge-aware Item Modeling for
Recommendation. Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, and
Shaoping Ma
- Evolutionary Product Description Generation: A Dynamic Fine-Tuning
Approach Leveraging User Click Behavior. Yongzhen Wang, Jian Wang, Heng
Huang, Hongsong Li, and Xiaozhong Liu
Session 1C (July 27,
9:40-11:40)
Graph-based Analysis
Chair: Qiaozhu Mei (University of Michigan)
- Pairwise View Weighted Graph Network for View-based 3D Model Retrieval.
Zan Gao, Yin-Ming Li, Wei-Li Guan, Wei-Zhi Nie, Zhi-Yong Cheng, and
An-An Liu
- Detecting User Community in Sparse Domain via Cross-Graph Pairwise
Learning. Zheng Gao, Hongsong Li, Zhuoren Jiang, and Xiaozhong Liu
- BiANE: Bipartite Attributed Network Embedding. Wentao Huang, Yuchen Li,
Yuan Fang, Ju Fan, and Hongxia Yang
- Hierarchical Fashion Graph Network for Personalised Outfit
Recommendation. Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun
Xiao, and Tat-Seng Chua
- Global Context Enhanced Graph Nerual Networks for Session-based
Recommendation. Ziyang Wang, Wei Wei, Cong Gao, Xiaoli Li, Xianling Mao,
and Minghui Qiu
- Interactive Recommender System via Knowledge Graph-enhanced
Reinforcement Learning. Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan
Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, and Yong Yu
Industrial Session I (July 27,
9:40-11:40)
Chair: Weinan Zhang (Shanghai Jiao Tong University)
- Invited talk: Large-scale Multi-modal Search and QA at
Alibaba. Rong Jin
- User Behavior Retrieval for Click-Through Rate Prediction. Jiarui Qin,
Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Yong Yu
- How Airbnb Tells You Will Enjoy Sunset Sailing in Barcelona?
Recommendation in a Two-Sided Travel Marketplace. Liang Wu and Mihajlo
Grbovic
Session 2A (July 27, 15:40-18:00)
Knowledge for Personalization
Chair: Imed Zitouni (Google)
- Jointly Non-Sampling Learning for Knowledge Graph Enhanced
Recommendation. Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and
Shaoping Ma
- AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order
Feature Interactions in CTR Prediction. Bin Liu, Niannan Xue, Huifeng
Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, and Zhenguo Li
- KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential
Recommendation. Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao,
Shaozhang Niu, and Jimmy Huang
- CKAN: Collaborative Knowledge-aware Attentive Network for Recommender
Systems. Ze Wang, Lin Guangyan, Huobin Tan, Qinghong Chen, and Xiyang
Liu
- CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect
Transfer Network. Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, and
Aixin Sun
- Leveraging Demonstrations for Reinforcement Recommendation Reasoning
over Knowledge Graphs. Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao,
Zheng Liu, Chunxiao Xing, and Xing Xie
- Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture
for Event Representation. Jianming Zheng, Fei Cai, and Honghui Chen
Session 2B (July 27,
15:40-18:00)
User Behavior and Experience
Chair: Suzan Verberne (Leiden University)
- Ranking-Incentivized Quality Preserving Content Modification. Gregory
Goren, Oren Kurland, Moshe Tennenholtz, and Fiana Raiber
- On Understanding Data Worker Interaction Behaviors. Lei Han, Tianwa
Chen, Gianluca Demartini, Marta Indulska, and Shazia Sadiq
- Creating a Children-Friendly Reading Environment via Joint Learning of
Content and Human Attention. Guoxiu He, Yangyang Kang, Zhuoren Jiang,
Jiawei Liu, Changlong Sun, Xiaozhong Liu, and Wei Lu
- Octopus: Comprehensive and Elastic User Representation for the
Generation of Recommendation Candidates. Zheng Liu, Junhan Yang, Jianxun
Lian, Defu Lian, and Xing Xie
- The Cortical Activity of Graded Relevance. Zuzana Pinkosova, William
McGeown, and Yashar Moshfeghi
- Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit
Feedback. Yuta Saito
- Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale
Users in Recommendation. Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng
Zhang, Xinxing Yu, Houzhi Shan, Yiqun Liu, and Shaoping Ma
Session 2C (July 27,
15:40-18:00)
Evaluation
Chair: Mark Sanderson (RMIT University)
- Measuring Recommendation Explanation Quality: The Conflicting Goals of
Explanations. Krisztian Balog and Filip Radlinski
- Bayesian Inferential Risk Evaluation on Multiple IR Systems. Rodger
Benham, Ben Carterette, J. Shane Culpepper, and Alistair Moffat
- How to Measure the Reproducibility of System-oriented IR Experiments.
Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai,
Philipp Schaer, and Ian Soboroff
- Good Evaluation Measures based on Document Preferences. Tetsuya Sakai
and Zhaohao Zeng
- Preference-based Evaluation Metrics for Web Image Search. Xiaohui Xie,
Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Haitian Chen, Min Zhang, and
Shaoping Ma
- Models Versus Satisfaction: Towards a Better Understanding of Evaluation
Metrics. Fan Zhang, Jiaxin Mao, Yiqun Liu, Xiaohui Xie, Weizhi Ma, Min
Zhang, and Shaoping Ma
- Cascade or Recency: Constructing Better Evaluation Metrics for Session
Search. Fan Zhang, Jiaxin Mao, Yiqun Liu, Weizhi Ma, Min Zhang, and
Shaoping Ma
Industrial Session II (July 27,
15:40-18:00)
Chair: Mounia Lalmas (Spotify)
- Efficient and Effective Query Auto-Completion. Simon Gog, Giulio Ermanno
Pibiri, and Rossano Venturini
- ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective
Recommendation. Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang
Zhang, and Wenwu Ou
- Multiplex Behavioral Relation Learning for Recommendation via Memory
Augmented Transformer Network. Lianghao Xia, Chao Huang, Yong Xu, Peng
Dai, Bo Zhang, and Liefeng Bo
- Entire Space Multi-Task Modeling via Post-Click Behavior Decomposition
for Conversion Rate Prediction. Hong Wen, Jing Zhang, Yuan Wang, Fuyu
Lv, Wentian Bao, Quan Lin, and Keping Yang
- Automated Embedding Size Search in Deep Recommender Systems. Haochen
Liu, Xiangyu Zhao, Chong Wang, Xiaobing Liu, and Jiliang Tang
Short/Demo/TOIS paper session I (July
27, 20:00-22:00)
Virtual Discussion Rooms are available.
Session 3A (July 27,
22:00-24:00)
Bias and Fairness
Chair: Ricardo Baeza-Yates (Northeastern University)
- Operationalizing the Legal Principle of Data Minimization for
Personalization. Asia J. Biega, Peter Potash, Hal Daumé III, Fernando
Diaz, and Michèle Finck
- Learning Personalized Risk Preferences for Recommendation. Yingqiang Ge,
Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, and Yongfeng Zhang
- Certifiable Robustness to Discrete Adversarial Perturbations for
Factorization Machines. Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He,
Carl Yang, and Zibin Zheng
- Controlling Fairness and Bias in Dynamic Ranking. Marco Morik, Ashudeep
Singh, Jessica Hong, and Thorsten Joachims
- Can the Crowd Identify Misinformation Objectively? The Effects of
Judgments Scale and Assessor's Bias. Kevin Roitero, Michael Soprano,
Shaoyang Fan, Damiano Spina, Stefano Mizzaro, and Gianluca Demartini
- Measuring and Mitigating Item Under-Recommendation Bias in Personalized
Ranking Systems. Ziwei Zhu, Jianling Wang, and James Caverlee
Session 3B (July 27,
22:00-24:00)
Learning to Rank
Chair: Hang Li (Bytedance)
- What Makes a Top-Performing Precision Medicine Search Engine? Tracing
Main
System Features in a Systematic Way. Erik Faessler, Michel Oleynik, and
Udo Hahn
- Accelerated Convergence for Counterfactual Learning to Rank. Rolf
Jagerman and Maarten de Rijke
- DVGAN: A Minimax Game for Search Result Diversification Combining
Explicit and Implicit Features. Jiongnan Liu, Zhicheng Dou, Xiaojie
Wang, Shuqi Lu, and Ji-Rong Wen
- Policy-Aware Unbiased Learning to Rank for Top-k Rankings. Harrie
Oosterhuis and Maarten de Rijke
- SetRank: Learning a Permutation-Invariant Ranking Model for Information
Retrieval. Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, and
Ji-Rong Wen
- Reinforcement Learning to Rank with Pairwise Policy Gradient. Jun Xu,
Zeng Wei, Long Xia, Yanyan Lan, Dawei Yin, Xueqi Cheng, and Ji-Rong Wen
Session 3C (July 27,
22:00-24:00)
Question Answering
Chair: Jochen L. Leidner (Refinitiv Labs / University of
Sheffield)
- Humor Detection in Product Question Answering Systems. Elad Kravi, David
Carmel, and Yftah Ziser
- Training Curricula for Open Domain Answer Re-Ranking. Sean MacAvaney,
Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli
Goharian, and Ophir Frieder
- Open-Retrieval Conversational Question Answering. Chen Qu, Liu Yang, Cen
Chen, Minghui Qiu, W. Bruce Croft, and Mohit Iyyer
- Learning to Ask Screening Questions for Job Postings. Baoxu Shi, Shan
Li, Jaewon Yang, Mustafa Emre Kazdagli, and Qi He
- Match$^2$: A Matching over Matching Model for Similar Question
Identification. Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing
Zhang, Yanyan Lan, Xueqi Cheng, Hui Jiang, and Xiaozhao Wang
- Answer Ranking for Product-Related Questions via Multiple Semantic
Relations Modeling. Wenxuan Zhang, Yang Deng, and Wai Lam
Tuesday, July 28, 2020
Session 4A (July 28,
9:40-11:40)
Query and Representation
Chair: Rob Capra (University of North Carolina)
- ESAM: Discriminative Domain Adaptation with Non-Displayed Items to
Improve Long-Tail Performance. Zhihong Chen, Rong Xiao, Chenliang Li,
Gangfeng Ye, Haochuan Sun, and Hongbo Deng
- Table Search Using a Deep Contextualized Language Model. Zhiyu Chen,
Mohamed Trabelsi, Jeff Heflin, Yinan Xu, and Brian Davison
- Convolutional Embedding for Edit Distance. Xinyan Dai, Xiao Yan, Kaiwen
Zhou, Yuxuan Wang, Han Yang, and James Cheng
- ASiNE: Adversarial Signed Network Embedding. Yeon-Chang Lee, Nayoun Seo,
Sang-Wook Kim, and Kyungsik Han
- Efficient Graph Query Processing over Geo-Distributed Datacenters. Ye
Yuan, Delong Ma, Zhenyu Wen, Yuliang Ma, Guoren Wang, and Lei Chen
- Spatio-Temporal Dual Graph Attention Network for Query-POI Matching.
Zixuan Yuan, Hao Liu, Yanchi Liu, Denghui Zhang, Fei Yi, Nengjun Zhu,
and Hui Xiong
Session 4B (July 28,
9:40-11:40)
Graph-based Recommendation
Chair: Chirag Shah (University of Washington)
- LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang
- GAME: Learning Graphical and Attentive Multi-view Embeddings for
Occasional Group Recommendation. Zhixiang He, Chi-Yin Chow, and Jia-Dong
Zhang
- Multi-behavior Recommendation with Graph Convolution Networks. Bowen
Jin, Chen Gao, Xiangnan He, Yong Li, and Depeng Jin
- GAG: Global Attributed Graph Neural Network for Streaming Session-based
Recommendation. Ruihong Qiu, Hongzhi Yin, Zi Huang, and Tong Chen
- Joint Item Recommendation and Attribute Inference: An Adaptive Graph
Convolutional Network Approach. Le Wu, Yonghui Yang, Kun Zhang, Richang
Hong, Yanjie Fu, and Meng Wang
- GCN-Based User Representation Learning for Unifying Robust
Recommendation and Fraudster Identification. Shijie Zhang, Hongzhi Yin,
Tong Chen, Nguyen Quoc Viet Hung, Zi Huang, and Lizhen Cui
Session 4C (July 28,
9:40-11:40)
Neural Networks and Embedding
Chair: Doug Oard (University of Maryland)
- Using Phoneme Representations to Build Predictive Models Robust to ASR
Errors. Simone Filice, Anjie Fang, Nut Limsopatham, and Oleg Rokhlenko
- Knowledge Enhanced Personalized Search. Shuqi Lu, Zhicheng Dou, Chenyan
Xiong, Xiaojie Wang, and Ji-Rong Wen
- Learning Dynamic Node Representations with Graph Neural Networks. Yao
Ma, Ziyi Guo, Zhaochun Ren, Jiliang Tang, and Dawei Yin
- An Eye Tracking Study of Web Search by People with and without Dyslexia.
Srishti Palani, Adam Fourney, Shane Williams, Kevin Larson, Irina
Spiridonova, and Meredith Ringel Morris
- DGL-KE: Training knowledge graph embeddings at scale. Da Zheng, Xiang
Song, Chao Ma, Zeyuan Tan, Zihao Ye, Hao Xiong, Zheng Zhang, and George
Karypis
- Neural Interactive Collaborative Filtering. Lixin Zou, Long Xia, Yulong
Gu, Weidong Liu, Dawei Yin, Jimmy Huang, and Xiangyu Zhao
Industrial Session III (July 28,
9:40-11:40)
Chair: Vanessa Murdock (Amazon)
- Invited talk: The New TREC Track on Podcast Search and Summarization.
Rosie Jones
- Think Beyond the Word: Understanding the Implied Textual Meaning by
Digesting Context, Local, and Noise. Guoxiu He, Zhe Gao, Zhuoren Jiang,
Yangyang Kang, Changlong Sun, Xiaozhong Liu, and Wei Lu
- Robust Layout-aware IE for Visually Rich Documents with Pre-trained
Language Models. Mengxi Wei, Yifan He, and Qiong Zhang
Session 5A (July 28,
15:40-17:40)
Domain Specific Applications 1
Chair: Elad Yom-Tov (Microsoft Research)
- Fashion Compatibility Modeling through a Multi-modal Try-on-guided
Scheme. Xue Dong, Jianlong Wu, Xuemeng Song, Hongjun Dai, and Liqiang
Nie
- Spatial Object Recommendation with Hints: When Spatial Granularity
Matters. Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane
Culpepper, Haochao Ying, Hao Liu, and Hui Xiong
- Product Bundle Identification using Semi-Supervised Learning. Hen
Tzaban, Ido Guy, Asnat Greenstein-Messica, Arnon Dagan, Lior Rokach, and
Bracha Shapira
- Coding Electronic Health Records with Adversarial Reinforcement Path
Generation. Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren,
Jian-Yun Nie, Jun Ma, and Maarten de Rijke
- Degree-Aware Alignment for Entities in Tail. Weixin Zeng, Xiang Zhao,
Wei Wang, Jiuyang Tang, and Zhen Tan
- Regional Relation Modeling for Visual Place Recognition. Yingying Zhu,
Biao Li, Jiong Wang, and Zhou Zhao
Session 5B (July 28,
15:40-17:40)
Learning for Recommendation
Chair: Nicola Ferro (University of Padua)
- A General Knowledge Distillation Framework for Counterfactual
Recommendation via Uniform Data. Dugang Liu, Pengxiang Cheng, Zhenhua
Dong, Xiuqiang He, Weike Pan, and Zhong Ming
- Agreement and Disagreement between True and False-Positive Metrics in
Recommender Systems Evaluation. Elisa Mena-Maldonado, Rocío Cañamares,
Pablo Castells, Yongli Ren, and Mark Sanderson
- Leveraging Social Media for Medical Text Simplification. Nikhil
Pattisapu, Nishant Prabhu, Smriti Bhati, and Vasudeva Varma
- Sampler Design for Implicit Feedback Data by Noisy-label Robust
Learning. Wenhui Yu and Zheng Qin
- MaHRL: Multi-goals Abstraction based Deep Hierarchical Reinforcement
Learning for Recommendations. Dongyang Zhao, Liang Zhang, Bo Zhang,
Lizhou Zheng, Yongjun Bao, and Weipeng Yan
- Towards Question-based Recommender Systems. Jie Zou, Yifan Chen, and
Evangelos Kanoulas
Session 5C (July 28,
15:40-17:40)
Information Access and Filtering
Chair: Djoerd Hiemstra (Radboud University)
- Try This Instead: Personalized and Interpretable Substitute
Recommendation. Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang,
and Meng Wang
- Towards Linking Camouflaged Descriptions to Implicit Products in
E-commerce. Longtao Huang, Bo Yuan, Rong Zhang, and Quan Lu
- Distributed Equivalent Substitution Training for Large-Scale Recommender
Systems. Haidong Rong, Yangzihao Wang, Feihu Zhou, Junjie Zhai, Haiyang
Wu, Rui Lan, Fan Li, Han Zhang, Yuekui Yang, Zhenyu Guo, and Di Wang
- Query Resolution for Conversational Search with Limited Supervision.
Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, and Maarten
de Rijke
- Self-Supervised Reinforcement Learning for Recommender Systems. Xin Xin,
Alexandros Karatzoglou, Ioannis Arapakis, and Joemon Jose
- Generative Attribute Manipulation Scheme for Flexible Fashion Search.
Xin Yang, Xuemeng Song, Xianjing Han, Haokun Wen, Jie Nie, and Liqiang
Nie
Industrial Session IV (July 28,
15:40-17:40)
Chair: Rui Wang (Alibaba Group)
- Understanding Echo Chambers in E-commerce Recommender Systems. Yingqiang
Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, and
Yongfeng Zhang
- Towards Personalized and Semantic Retrieval: An End-to-End Solution for
E-commerce Search via Embedding Learning. Han Zhang, Songlin Wang, Kang
Zhang, Zhiling Tang, Yunjiang Jiang, Yun Xiao, Paul Yan, and Wen-Yun
Yang
- GMCM: Graph-based Micro-behavior Conversion Model for Post-click
Conversion Rate Estimation. Wentian Bao, Hong Wen, Sha Li, Xiao-Yang
Liu, Quan Lin, and Keping Yang
- A Heterogeneous Information Network based Cross Domain Insurance
Recommendation System for Cold Start Users. Ye Bi, Liqiang Song, Mengqiu
Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao
- Item Tagging for Information Retrieval: A Tripartite Graph Neural
Network based Approach. Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu,
Ruiming Tang, and Xiuqiang He
Short/Demo/TOIS Paper Session II (July
28, 20:00-22:00)
Virtual Discussion Rooms are available.
Session 6A (July 28,
22:00-24:00)
Neural Collaborative Filtering 1
Chair: Min Zhang (Tsinghua University)
- How Dataset Characteristics Affect the Robustness of Collaborative
Recommendation Models. Yashar Deldjoo, Tommaso Di Noia, Felice Antonio
Merra, Eugenio Di Sciascio
- DPLCF: Differentially Private Local Collaborative Filtering. Chen Gao,
Chao Huang, Dongsheng Lin, Yong Li, and Depeng Jin
- Content-aware Neural Hashing for Cold-start Recommendation. Casper
Hansen, Christian Hansen, Jakob Grue Simonsen Stephen Alstrup, and
Christina Lioma
- Meta Matrix Factorization for Federated Rating Predictions. Yujie Lin,
Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de
Rijke, and Xiuzhen Cheng
- The Impact of More Transparent Interfaces on Behavior in Personalized
Recommendation. Tobias Schnabel, Paul Bennett, Saleema Amershi, Peter
Bailey, and Thorsten Joachims
- Disentangled Representations for Graph-based Collaborative Filtering.
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng
Chua
Session 6B (July 28,
22:00-24:00)
Domain Specific Applications 2
Chair: Krisztian Balog (University of Stavanger)
- Domain-Adaptive Neural Automated Essay Scoring. Yue Cao, Hanqi Jin,
Xiaojun Wan, and Zhiwei Yu
- ADORE: Aspect Dependent Online REview Labeling for Review Generation.
Parisa Kaghazgaran, Jianling Wang, Ruihong Huang, and James Caverlee
- Finding the Best of Two Worlds: Faster and More Robust Top-k Document
Retrieval. Omar Khattab, Mohammad Hammoud, and Tamer Elsayed
- Recommending Podcasts for Cold-Start Users Based on Music Listening and
Taste. Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin
Laurent, Denis Charrier, Briana Vecchione, and Benjamin Carterette
- Learning with Weak Supervision for Email Intent Detection. Kai Shu,
Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad
Shokouhi, and Susan Dumais
- 3D Self-Attention for Unsupervised Video Quantization. Jingkuan Song,
Ruimin Lang, Xiaosu Zhu, Xing Xu, Lianli Gao, and Heng Tao Shen
Session 6C (July 28,
22:00-24:00)
Context-aware Modeling
Chair: Carsten Eickhoff (Brown University)
- Modeling Personalized Item Frequency Information for Next-basket
Recommendation. Haoji Hu, Xiangnan He, Jinyang Gao, and Zhi-Li Zhang
- Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain
Recommendation. Adit Krishnan, Mahashweta Das, Mangesh Bendre, Hao Yang,
and Hari Sundaram
- Incorporating User Micro-behaviors and Item Knowledge into Multi-task
Learning for Session-based Recommendation. Wenjing Meng, Deqing Yang,
and Yanghua Xiao
- Next-item Recommendation with Sequential Hypergraphs. Jianling Wang,
Kaize Ding, Liangjie Hong, Huan Liu, and James Caverlee
- Encoding History with Context-aware Representation Learning for
Personalized Search. Yujia Zhou, Zhicheng Dou, and Ji-Rong Wen
- Recommendation for New Users and New Items via Randomized Training and
Mixture-of-Experts Transformation. Ziwei Zhu, Shahin Sefati, Parsa
Saadatpanah, and James Caverlee
Wednesday, July 29, 2020
Session 7A (July 29,
9:40-11:40)
Conversation and Interactive IR
Chair: Jeff Dalton (University of Glasgow)
- Neural Representation Learning for Clarification in Conversational
Search. Helia Hashemi, Hamed Zamani, and Bruce Croft
- Investigating Reference Dependence Effects on User Search Interaction
and Satisfaction. Jiqun Liu and Fangyuan Han
- DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded
Conversation. Chuan Meng, Pengjie Ren, Zhumin Chen, Weiwei Sun, Zhaochun
Ren, Zhaopeng Tu, and Maarten de Rijke
- What If Bots Feel Moods? Towards Controllable Retrieval-based Dialogue
Systems with Emotion-Aware Transition Networks. Lisong Qiu, Ying Wai
Shiu, Pingping Lin, Ruihua Song, Yue Liu, Dongyan Zhao, and Rui Yan
- Expressions of Style in Information Seeking Conversation with an Agent.
Paul Thomas, Daniel Mcduff, Mary Czerwinski, and Nick Craswell
- Analyzing and Learning from User Interactions for Search Clarification.
Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz,
Paul Bennett, Nick Craswell, and Susan Dumais
Session 7B (July 29,
9:40-11:40)
Text Classification and Transfer Learning
Chair: Rosie Jones (Spotify)
- A Unified Dual-view Model for Review Summarization and Sentiment
Classification with Inconsistency Loss. Hou Pong Chan, Wang Chen, and
Irwin King
- Enhancing Text Classification via Discovering Additional Semantic Clues
from Logograms. Chen Qian, Fuli Feng, Lijie Wen, Li Lin, and Tat-Seng
Chua
- Learning to Transfer Graph Embeddings for Inductive Graph based
Recommendation. Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong,
and Meng Wang
- Web-to-Voice Transfer for Product Recommendation on Voice. Rongting
Zhang and Jie Yang
- Minimally Supervised Categorization of Text with Metadata. Yu Zhang, Yu
Meng, Jiaxin Huang, Frank F. Xu, Xuan Wang, and Jiawei Han
- Joint Aspect-Sentiment Analysis with Minimal User Guidance. Honglei
Zhuang, Fang Guo, Chao Zhang, Liyuan Liu, and Jiawei Han
Session 7C (July 29,
9:40-11:40)
Neural Collaborative Filtering 2
Chair: Yi Fang (Santa Clara University)
- AR-CF: Augmenting Virtual Users and Items in Collaborative Filtering for
Addressing Cold-Start Problems. Dong-Kyu Chae, Jihoo Kim, Sang-Wook Kim,
and Duen Horng Chau
- Studying Product Competition Using Representation Learning. Fanglin
Chen, Xiao Liu, Davide Proserpio, Isamar Troncoso, and Feiyu Xiong
- Deep Critiquing for VAE-based Recommender Systems. Kai Luo, Hojin Yang,
Ga Wu, and Scott Sanner
- GroupIM: A Mutual Information Maximizing Framework for Neural Group
Recommendation. Aravind Sankar, Yanhong Wu, Yuhang Wu, Wei Zhang, Hao
Yang, and Hari Sundaram
- Neighbor Interaction Aware Graph Convolution Networks for
Recommendation. Jianing Sun, Yingxue Zhang, Wei Guo, Huifeng Guo,
Ruiming Tang, Xiuqiang He, Chen Ma, and Mark Coates
- A General Network Compression Framework for Sequential Recommender
Systems. Yang Sun, Fajie Yuan, Min Yang, Guoao Wei, Zhou Zhao, and Duo
Liu
Industrial Session V (July 29,
9:40-11:40)
Chair: Imed Zitouni (Google)
- A Counterfactual Framework for Seller-Side A/B Testing on Marketplaces.
Viet Ha-Thuc, Avishek Dutta, Ren Mao, Matthew Wood, and Yunli Liu
- Knowledge Graph-based Event Embedding Framework for Financial
Quantitative Investments. Dawei Cheng, Fangzhou Yang, Xiaoyang Wang,
Ying Zhang, and Liqing Zhang
- FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal
Retrieval. Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Yi Wei, Yi Hu,
and Hao Wang
- Large Scale Abstractive Multi-Review Summarization (LSARS) via Aspect
Alignment. Haojie Pan, Rongqin Yang, Xin Zhou, Rui Wang, Deng Cai, and
Xiaozhong Liu
- Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to
Intervene Novel Coronavirus COVID-19 Outbreak. Zhenxin Fu, Yu Wu, Hailei
Zhang, Yichuan Hu, Dongyan Zhao, and Rui Yan
Session 8A (July 29,
15:40-17:40)
Domain Specific Retrieval Tasks
Chair: David Carmel (Amazon)
- Learning Efficient Representations of Mouse Movements toPredict User
Attention in Sponsored Search. Ioannis Arapakis and Luis A. Leiva
- Query Reformulation in E-Commerce Search. Sharon Hirsch, Ido Guy,
Alexander Nus, Arnon Dagan, and Oren Kurland
- Generating Images Instead of Retrieving them: Relevance feedback on
Generative Adversarial Networks. Antti Ukkonen, Pyry Joona, and Tuukka
Ruotsalo
- Tree-augmented Cross-Modal Encoding for Complex-Query Video Retrieval.
Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, and Tat-Seng
Chua
- Nonlinear Robust Discrete Hashing for Cross-Modal Retrieval. Zhan Yang,
Jun Long, Lei Zhu, and Wenti Huang
- Employing Personal Word Embeddings for Personalized Search. Jing Yao,
Zhicheng Dou, and Ji-Rong Wen
Session 8B (July 29,
15:40-17:40)
Multi-modal Retrieval and Ranking
Chair: Benjamin Piwowarski (CNRS)
- Query Rewriting for Voice Shopping Null Queries. Iftah Gamzu, Marina
Haikin, and Nissim Halabi
- Joint-modal Distribution-based Similarity Hashing for Large-scale
Unsupervised Deep Cross-modal Retrieval. Song Liu, Shengsheng Qian, Yang
Guan, Jiawei Zhan, and Long Ying
- Learning Colour Representations of Search Queries. Paridhi Maheshwari,
Manoj Ghuhan A, and Vishwa Vinay
- Web Table Retrieval using Multimodal Deep Learning. Roee Shraga, Haggai
Roitman, Guy Feigenblat, and Mustafa Canim
- Online Collective Matrix Factorization Hashing for Large-Scale
Cross-Media Retrieval. Di Wang, Quan Wang, Yaqiang An, Xinbo Gao, and
Yumin Tian
- Correlated Features Synthesis and Alignment for Zero-shot Cross-modal
Retrieval. Xing Xu, Kaiyi Lin, Huimin Lu, Lianli Gao, and Heng Tao Shen
Session 8C (July 29,
15:40-17:40)
Sequential Recommendation
Chair: Josiane Mothe (University of Toulouse)
- HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation.
Shanshan Feng, Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, and Fan Li
- Dual Sequential Network for Temporal Sets Prediction. Leilei Sun,
Yansong Bai, Bowen Du, Chuanren Liu, Hui Xiong, and Weifeng Lv
- Group-Aware Long- and Short-Term Graph Representation Learning for
Sequential Group Recommendation. Wen Wang, Wei Zhang, Jun Rao, Zhijie
Qiu, Bo Zhang, Leyu Lin, and Hongyuan Zha
- Time Matters: Sequential Recommendation with Complex Temporal
Information. Wenwen Ye, Shuaiqiang Wang, Xu Chen, Xuepeng Wang, Zheng
Qin, and Dawei Yin
- Parameter-Efficient Transfer from Sequential Behaviors for User Modeling
and Recommendation. Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and
Liguang Zhang
- How to Retrain a Recommender System? Yang Zhang, Xiangnan He, Fuli Feng,
Chenxu Wang, Meng Wang, Yan Li, and Yongdong Zhang
Industrial Session VI (July 29,
15:40-17:40)
Chair: Elad Yom-Tov (Microsoft Research)
- Network on Network for Tabular Data Classification in Real-world
Applications, Yuanfei Luo, Hao Zhou, Weiwei Tu, Yuqiang Chen, Wenyuan
Dai, and Qiang Yang
- Identifying Tasks from Mobile App Usage Patterns. Yuan Tian, Ke Zhou,
Mounia Lalmas, and Dan Pelleg
- Efficient Image Gallery Representations at Scale through Multi-task
Learning. Benjamin Gutelman and Pavel Levin