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ORCID43rd SIGIR 2020: Virtual Event, China
- Jimmy X. Huang
, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, Yiqun Liu:
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020. ACM 2020, ISBN 978-1-4503-8016-4
Keynotes and Invited Talks
- Geoffrey E. Hinton:
The Next Generation of Neural Networks. 1 - Norbert Fuhr:
Proof by Experimentation? Towards Better IR Research. 2 - Ellen M. Voorhees:
Coopetition in IR Research. 3 - Zongben Xu:
On Presuppositions of Machine Learning: A Meta Theory. 4 - Dacheng Tao:
How Deep Learning Works for Information Retrieval. 5 - Elizabeth F. Churchill:
From Information to Assistance. 6 - Rosie Jones:
The New TREC Track on Podcast Search and Summarization. 7 - Rong Jin:
Large-scale Multi-modal Search and QA at Alibaba. 8
Session 1A: NeuIR and Semantic Matching
- Hamed R. Bonab, Sheikh Muhammad Sarwar, James Allan:
Training Effective Neural CLIR by Bridging the Translation Gap. 9-18 - Yongyu Jiang, Peng Zhang, Hui Gao, Dawei Song:
A Quantum Interference Inspired Neural Matching Model for Ad-hoc Retrieval. 19-28 - Jiarui Jin, Yuchen Fang, Weinan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai:
A Deep Recurrent Survival Model for Unbiased Ranking. 29-38 - Omar Khattab, Matei Zaharia:
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. 39-48 - Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder:
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations. 49-58 - Ali Montazeralghaem, Hamed Zamani, James Allan:
A Reinforcement Learning Framework for Relevance Feedback. 59-68
Session 1B: Knowledge and Explainability
- Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, Gerard de Melo:
Fairness-Aware Explainable Recommendation over Knowledge Graphs. 69-78 - Jibing Gong, Shen Wang, Jinlong Wang, Wenzheng Feng, Hao Peng, Jie Tang, Philip S. Yu:
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View. 79-88 - Ruiyang Ren, Zhaoyang Liu, Yaliang Li, Wayne Xin Zhao, Hui Wang, Bolin Ding, Ji-Rong Wen:
Sequential Recommendation with Self-Attentive Multi-Adversarial Network. 89-98 - Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, Lun-Wei Ku:
MVIN: Learning Multiview Items for Recommendation. 99-108 - Chenyang Wang, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma:
Make It a Chorus: Knowledge- and Time-aware Item Modeling for Sequential Recommendation. 109-118 - Yongzhen Wang, Jian Wang, Heng Huang, Hongsong Li, Xiaozhong Liu:
Evolutionary Product Description Generation: A Dynamic Fine-Tuning Approach Leveraging User Click Behavior. 119-128
Session 1C: Graph-based Analysis
- Zan Gao, Yin-Ming Li, Weili Guan, Weizhi Nie, Zhiyong Cheng, Anan Liu:
Pairwise View Weighted Graph Network for View-based 3D Model Retrieval. 129-138 - Zheng Gao, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu:
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning. 139-148 - Wentao Huang, Yuchen Li, Yuan Fang, Ju Fan, Hongxia Yang:
BiANE: Bipartite Attributed Network Embedding. 149-158 - Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua:
Hierarchical Fashion Graph Network for Personalized Outfit Recommendation. 159-168 - Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xianling Mao, Minghui Qiu:
Global Context Enhanced Graph Neural Networks for Session-based Recommendation. 169-178 - Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu:
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning. 179-188
Session 2A: Knowledge for Personalization
- Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma:
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation. 189-198 - Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, Zhenguo Li:
AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction. 199-208 - Pengfei Wang, Yu Fan, Long Xia, Wayne Xin Zhao, Shaozhang Niu, Jimmy X. Huang:
KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation. 209-218 - Ze Wang, Guangyan Lin, Huobin Tan, Qinghong Chen, Xiyang Liu:
CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems. 219-228 - Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun:
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network. 229-238 - Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie:
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs. 239-248 - Jianming Zheng, Fei Cai, Honghui Chen:
Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation. 249-258
Session 2B: User Behavior and Experience
- Gregory Goren, Oren Kurland, Moshe Tennenholtz, Fiana Raiber:
Ranking-Incentivized Quality Preserving Content Modification. 259-268 - Lei Han, Tianwa Chen, Gianluca Demartini, Marta Indulska, Shazia W. Sadiq:
On Understanding Data Worker Interaction Behaviors. 269-278 - Guoxiu He, Yangyang Kang, Zhuoren Jiang, Jiawei Liu, Changlong Sun, Xiaozhong Liu, Wei Lu:
Creating a Children-Friendly Reading Environment via Joint Learning of Content and Human Attention. 279-288 - Zheng Liu, Jianxun Lian, Junhan Yang, Defu Lian, Xing Xie:
Octopus: Comprehensive and Elastic User Representation for the Generation of Recommendation Candidates. 289-298 - Zuzana Pinkosova, William J. McGeown, Yashar Moshfeghi:
The Cortical Activity of Graded Relevance. 299-308 - Yuta Saito:
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. 309-318 - Shaoyun Shi, Weizhi Ma, Min Zhang, Yongfeng Zhang, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma:
Beyond User Embedding Matrix: Learning to Hash for Modeling Large-Scale Users in Recommendation. 319-328
Session 2C: Evaluation
- Krisztian Balog, Filip Radlinski:
Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations. 329-338 - Rodger Benham, Ben Carterette, J. Shane Culpepper, Alistair Moffat:
Bayesian Inferential Risk Evaluation On Multiple IR Systems. 339-348 - Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff:
How to Measure the Reproducibility of System-oriented IR Experiments. 349-358 - Tetsuya Sakai, Zhaohao Zeng:
Good Evaluation Measures based on Document Preferences. 359-368 - Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Haitian Chen, Min Zhang, Shaoping Ma:
Preference-based Evaluation Metrics for Web Image Search. 369-378 - Fan Zhang, Jiaxin Mao, Yiqun Liu, Xiaohui Xie, Weizhi Ma, Min Zhang, Shaoping Ma:
Models Versus Satisfaction: Towards a Better Understanding of Evaluation Metrics. 379-388 - Fan Zhang, Jiaxin Mao, Yiqun Liu, Weizhi Ma, Min Zhang, Shaoping Ma:
Cascade or Recency: Constructing Better Evaluation Metrics for Session Search. 389-398
Session 3A: Bias and Fairness
- Asia J. Biega, Peter Potash, Hal Daumé III, Fernando Diaz, Michèle Finck:
Operationalizing the Legal Principle of Data Minimization for Personalization. 399-408 - Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Zuohui Fu, Fei Sun, Yongfeng Zhang:
Learning Personalized Risk Preferences for Recommendation. 409-418 - Yang Liu, Xianzhuo Xia, Liang Chen, Xiangnan He, Carl Yang, Zibin Zheng:
Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines. 419-428 - Marco Morik, Ashudeep Singh, Jessica Hong, Thorsten Joachims:
Controlling Fairness and Bias in Dynamic Learning-to-Rank. 429-438 - Kevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina, Stefano Mizzaro, Gianluca Demartini:
Can The Crowd Identify Misinformation Objectively?: The Effects of Judgment Scale and Assessor's Background. 439-448 - Ziwei Zhu, Jianling Wang, James Caverlee:
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. 449-458
Session 3B: Learning to Rank
- Erik Faessler, Michel Oleynik, Udo Hahn:
What Makes a Top-Performing Precision Medicine Search Engine?: Tracing Main System Features in a Systematic Way. 459-468 - Rolf Jagerman, Maarten de Rijke:
Accelerated Convergence for Counterfactual Learning to Rank. 469-478 - Jiongnan Liu, Zhicheng Dou, Xiaojie Wang, Shuqi Lu, Ji-Rong Wen:
DVGAN: A Minimax Game for Search Result Diversification Combining Explicit and Implicit Features. 479-488 - Harrie Oosterhuis, Maarten de Rijke:
Policy-Aware Unbiased Learning to Rank for Top-k Rankings. 489-498 - Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, Jirong Wen:
SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval. 499-508 - Jun Xu, Zeng Wei, Long Xia, Yanyan Lan, Dawei Yin, Xueqi Cheng, Ji-Rong Wen:
Reinforcement Learning to Rank with Pairwise Policy Gradient. 509-518
Session 3C: Question Answering
- Yftah Ziser, Elad Kravi, David Carmel:
Humor Detection in Product Question Answering Systems. 519-528 - Sean MacAvaney, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, Nazli Goharian, Ophir Frieder:
Training Curricula for Open Domain Answer Re-Ranking. 529-538 - Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, Mohit Iyyer:
Open-Retrieval Conversational Question Answering. 539-548 - Baoxu Shi, Shan Li, Jaewon Yang, Mustafa Emre Kazdagli, Qi He:
Learning to Ask Screening Questions for Job Postings. 549-558 - Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xueqi Cheng, Hui Jiang, Xiaozhao Wang:
Match²: A Matching over Matching Model for Similar Question Identification. 559-568 - Wenxuan Zhang, Yang Deng, Wai Lam:
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling. 569-578
Session 4A: Query and Representation
- Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, Hongbo Deng:
ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance. 579-588 - Zhiyu Chen, Mohamed Trabelsi, Jeff Heflin, Yinan Xu, Brian D. Davison:
Table Search Using a Deep Contextualized Language Model. 589-598 - Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng:
Convolutional Embedding for Edit Distance. 599-608 - Yeon-Chang Lee, Nayoun Seo, Kyungsik Han, Sang-Wook Kim:
ASiNE: Adversarial Signed Network Embedding. 609-618 - Ye Yuan, Delong Ma, Zhenyu Wen, Yuliang Ma, Guoren Wang, Lei Chen:
Efficient Graph Query Processing over Geo-Distributed Datacenters. 619-628 - Zixuan Yuan, Hao Liu, Yanchi Liu, Denghui Zhang, Fei Yi, Nengjun Zhu, Hui Xiong:
Spatio-Temporal Dual Graph Attention Network for Query-POI Matching. 629-638
Session 4B: Graph-based Recommendation
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, Meng Wang:
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. 639-648 - Zhixiang He, Chi-Yin Chow, Jia-Dong Zhang:
GAME: Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation. 649-658 - Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, Yong Li:
Multi-behavior Recommendation with Graph Convolutional Networks. 659-668 - Ruihong Qiu, Hongzhi Yin, Zi Huang, Tong Chen:
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation. 669-678 - Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, Meng Wang:
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach. 679-688 - Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Zi Huang, Lizhen Cui:
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection. 689-698
Session 4C: Neural Networks and Embedding
- Anjie Fang, Simone Filice, Nut Limsopatham, Oleg Rokhlenko:
Using Phoneme Representations to Build Predictive Models Robust to ASR Errors. 699-708 - Shuqi Lu, Zhicheng Dou, Chenyan Xiong, Xiaojie Wang, Ji-Rong Wen:
Knowledge Enhanced Personalized Search. 709-718 - Yao Ma, Ziyi Guo, Zhaochun Ren, Jiliang Tang, Dawei Yin:
Streaming Graph Neural Networks. 719-728 - Srishti Palani, Adam Fourney, Shane Williams, Kevin Larson, Irina Spiridonova, Meredith Ringel Morris:
An Eye Tracking Study of Web Search by People With and Without Dyslexia. 729-738 - Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, George Karypis:
DGL-KE: Training Knowledge Graph Embeddings at Scale. 739-748 - Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, Dawei Yin:
Neural Interactive Collaborative Filtering. 749-758
Session 5A: Domain Specific Applications 1
- Xue Dong, Jianlong Wu, Xuemeng Song, Hongjun Dai, Liqiang Nie:
Fashion Compatibility Modeling through a Multi-modal Try-on-guided Scheme. 771-780 - Hui Luo, Jingbo Zhou, Zhifeng Bao, Shuangli Li, J. Shane Culpepper, Haochao Ying, Hao Liu, Hui Xiong:
Spatial Object Recommendation with Hints: When Spatial Granularity Matters. 781-790 - Hen Tzaban, Ido Guy, Asnat Greenstein-Messica, Arnon Dagan, Lior Rokach, Bracha Shapira:
Product Bundle Identification using Semi-Supervised Learning. 791-800 - Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jian-Yun Nie, Jun Ma, Maarten de Rijke:
Coding Electronic Health Records with Adversarial Reinforcement Path Generation. 801-810 - Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan:
Degree-Aware Alignment for Entities in Tail. 811-820 - Yingying Zhu, Biao Li, Jiong Wang, Zhou Zhao:
Regional Relation Modeling for Visual Place Recognition. 821-830
Session 5B: Learning for Recommendation
- Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, Zhong Ming:
A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. 831-840 - Elisa Mena-Maldonado, Rocío Cañamares, Pablo Castells, Yongli Ren, Mark Sanderson:
Agreement and Disagreement between True and False-Positive Metrics in Recommender Systems Evaluation. 841-850 - Nikhil Pattisapu, Nishant Prabhu, Smriti Bhati, Vasudeva Varma:
Leveraging Social Media for Medical Text Simplification. 851-860 - Wenhui Yu, Zheng Qin:
Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning. 861-870 - Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng Yan:
MaHRL: Multi-goals Abstraction Based Deep Hierarchical Reinforcement Learning for Recommendations. 871-880 - Jie Zou, Yifan Chen, Evangelos Kanoulas:
Towards Question-based Recommender Systems. 881-890
Session 5C: Information Access and Filtering
- Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, Meng Wang:
Try This Instead: Personalized and Interpretable Substitute Recommendation. 891-900 - Longtao Huang, Bo Yuan, Rong Zhang, Quan Lu:
Towards Linking Camouflaged Descriptions to Implicit Products in E-commerce. 901-910 - Haidong Rong, Yangzihao Wang, Feihu Zhou, Junjie Zhai, Haiyang Wu, Rui Lan, Fan Li, Han Zhang, Yuekui Yang, Zhenyu Guo, Di Wang:
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems. 911-920 - Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, Maarten de Rijke:
Query Resolution for Conversational Search with Limited Supervision. 921-930 - Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose:
Self-Supervised Reinforcement Learning for Recommender Systems. 931-940 - Xin Yang, Xuemeng Song, Xianjing Han, Haokun Wen, Jie Nie, Liqiang Nie:
Generative Attribute Manipulation Scheme for Flexible Fashion Search. 941-950
Session 6A: Neural Collaborative Filtering 1
- Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Felice Antonio Merra:
How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models. 951-960 - Chen Gao, Chao Huang, Dongsheng Lin, Depeng Jin, Yong Li:
DPLCF: Differentially Private Local Collaborative Filtering. 961-970 - Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma:
Content-aware Neural Hashing for Cold-start Recommendation. 971-980 - Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, Xiuzhen Cheng:
Meta Matrix Factorization for Federated Rating Predictions. 981-990 - Tobias Schnabel, Saleema Amershi, Paul N. Bennett, Peter Bailey, Thorsten Joachims:
The Impact of More Transparent Interfaces on Behavior in Personalized Recommendation. 991-1000 - Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, Tat-Seng Chua:
Disentangled Graph Collaborative Filtering. 1001-1010
Session 6B: Domain Specific Applications 2
- Yue Cao, Hanqi Jin, Xiaojun Wan, Zhiwei Yu:
Domain-Adaptive Neural Automated Essay Scoring. 1011-1020 - Parisa Kaghazgaran, Jianling Wang, Ruihong Huang, James Caverlee:
ADORE: Aspect Dependent Online REview Labeling for Review Generation. 1021-1030 - Omar Khattab, Mohammad Hammoud, Tamer Elsayed:
Finding the Best of Both Worlds: Faster and More Robust Top-k Document Retrieval. 1031-1040 - Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione, Ben Carterette:
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste. 1041-1050 - Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, Susan T. Dumais:
Learning with Weak Supervision for Email Intent Detection. 1051-1060 - Jingkuan Song, Ruimin Lang, Xiaosu Zhu, Xing Xu, Lianli Gao, Heng Tao Shen:
3D Self-Attention for Unsupervised Video Quantization. 1061-1070
Session 6C: Context-aware Modeling
- Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang:
Modeling Personalized Item Frequency Information for Next-basket Recommendation. 1071-1080