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ORCID16th WSDM 2023: Singapore
- Tat-Seng Chua, Hady W. Lauw, Luo Si, Evimaria Terzi, Panayiotis Tsaparas:
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023. ACM 2023, ISBN 978-1-4503-9407-9
Keynote Talks
- Ya-Qin Zhang
:
Towards Autonomous Driving. 1 - Maarten de Rijke:
Beyond-Accuracy Goals, Again. 2-3 - Rosie Jones:
Learning to Understand Audio and Multimodal Content. 4-5
Session 1: Social Issues in Data Mining
- Nikita Bhalla, Adam Lechowicz, Cameron Musco:
Local Edge Dynamics and Opinion Polarization. 6-14 - Sunyoung Park, Kyuri Choi, Haeun Yu, Youngjoong Ko:
Never Too Late to Learn: Regularizing Gender Bias in Coreference Resolution. 15-23 - Tao Yang, Zhichao Xu, Zhenduo Wang, Anh Tran, Qingyao Ai:
Marginal-Certainty-Aware Fair Ranking Algorithm. 24-32 - Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars, Federico Bianchi, Patrícia G. C. Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev:
Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion. 33-41 - Srinivas Virinchi, Anoop Saladi:
BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs. 42-50 - Xing Su, Jian Yang, Jia Wu, Yuchen Zhang:
Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks. 51-59
Session 2: Recommender Systems I
- Li He, Xianzhi Wang, Dingxian Wang, Haoyuan Zou, Hongzhi Yin, Guandong Xu:
Simplifying Graph-based Collaborative Filtering for Recommendation. 60-68 - Kang Li, Chang-Dong Wang, Jian-Huang Lai, Huaqiang Yuan:
Self-Supervised Group Graph Collaborative Filtering for Group Recommendation. 69-77 - Jiangxia Cao, Shaoshuai Li, Bowen Yu, Xiaobo Guo, Tingwen Liu, Bin Wang:
Towards Universal Cross-Domain Recommendation. 78-86 - Mozhdeh Ariannezhad, Ming Li, Sebastian Schelter, Maarten de Rijke:
A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping. 87-95 - Riwei Lai, Li Chen, Yuhan Zhao, Rui Chen, Qilong Han:
Disentangled Negative Sampling for Collaborative Filtering. 96-104 - Mukun Chen, Xiuwen Gong, YH Jin, Wenbin Hu:
Relation Preference Oriented High-order Sampling for Recommendation. 105-113
Session 3: Graph Neural Networks
- Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Hao Peng, Angsheng Li, Shan Xue, Jianlin Su:
Minimum Entropy Principle Guided Graph Neural Networks. 114-122 - Cheng Yang, Yuxin Guo, Yao Xu, Chuan Shi, Jiawei Liu, Chunchen Wang, Xin Li, Ning Guo, Hongzhi Yin:
Learning to Distill Graph Neural Networks. 123-131 - Wendong Bi, Lun Du, Qiang Fu, Yanlin Wang, Shi Han, Dongmei Zhang:
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature Distribution. 132-140 - Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, Ambuj K. Singh:
Global Counterfactual Explainer for Graph Neural Networks. 141-149 - Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu:
Effective Graph Kernels for Evolving Functional Brain Networks. 150-158 - Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye:
Self-Supervised Graph Structure Refinement for Graph Neural Networks. 159-167
Session 4: Best of WSDM 2023
- Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim:
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network. 168-176 - John Pougué-Biyong, Akshay Gupta, Aria Haghighi, Ahmed El-Kishky:
Learning Stance Embeddings from Signed Social Graphs. 177-185 - Tianchi Cai, Jiyan Jiang, Wenpeng Zhang, Shiji Zhou, Xierui Song, Li Yu, Lihong Gu, Xiaodong Zeng, Jinjie Gu, Guannan Zhang:
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning. 186-194 - Hongrui Xuan, Yi Liu, Bohan Li, Hongzhi Yin:
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation. 195-203
Session 5: Recommender Systems II
- Jiarui Qin, Jiachen Zhu, Yankai Liu, Junchao Gao, Jianjie Ying, Chaoxiong Liu, Ding Wang, Junlan Feng, Chao Deng, Xiaozheng Wang, Jian Jiang, Cong Liu, Yong Yu, Haitao Zeng, Weinan Zhang:
Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation. 204-212 - Hongwei Tang, Detian Zhang:
Range Restricted Route Recommendation Based on Spatial Keyword. 213-221 - Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu:
Meta Policy Learning for Cold-Start Conversational Recommendation. 222-230 - Xiaoyu Zhang, Xin Xin, Dongdong Li, Wenxuan Liu, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren:
Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation. 231-239 - Wei Cai, Fuli Feng, Qifan Wang, Tian Yang, Zhenguang Liu, Congfu Xu:
A Causal View for Item-level Effect of Recommendation on User Preference. 240-248 - Jianchao Ji, Zelong Li, Shuyuan Xu, Max Xiong, Juntao Tan, Yingqiang Ge, Hao Wang, Yongfeng Zhang:
Counterfactual Collaborative Reasoning. 249-257
Session 6: Learning
- Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma:
NGAME: Negative Mining-aware Mini-batching for Extreme Classification. 258-266 - Xuanhao Chen, Liwei Deng, Yan Zhao, Kai Zheng:
Adversarial Autoencoder for Unsupervised Time Series Anomaly Detection and Interpretation. 267-275 - Song Wang, Yushun Dong, Kaize Ding, Chen Chen, Jundong Li:
Few-shot Node Classification with Extremely Weak Supervision. 276-284 - Donghyun Son, Byounggyu Lew, Kwanghee Choi, Yongsu Baek, Seungwoo Choi, Beomjun Shin, Sungjoo Ha, Buru Chang:
Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild. 285-293 - Xu Wang, Lianliang Chen, Hongbo Zhang, Pengkun Wang, Zhengyang Zhou, Yang Wang:
A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework. 294-302 - Chakib Fettal, Lazhar Labiod, Mohamed Nadif:
Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering. 303-311
Session 7: Graph Mining
- Suman K. Bera, Jayesh Choudhari, Shahrzad Haddadan, Sara Ahmadian:
DeMEtRIS: Counting (near)-Cliques by Crawling. 312-320 - Qiang Yang, Changsheng Ma, Qiannan Zhang, Xin Gao, Chuxu Zhang, Xiangliang Zhang:
Interpretable Research Interest Shift Detection with Temporal Heterogeneous Graphs. 321-329 - Zequan Xu, Lianyun Li, Hui Li, Qihang Sun, Shaofeng Hu, Rongrong Ji:
Self-supervised Graph Representation Learning for Black Market Account Detection. 330-338 - Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan:
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection. 339-347 - Yiwei Wang, Bryan Hooi, Yozen Liu, Neil Shah:
Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference. 348-356 - Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang:
Alleviating Structural Distribution Shift in Graph Anomaly Detection. 357-365
Session 8: Recommendation and Learning
- Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu, Zang Li, Guoqiang Shu, Xiaohu Qie, Di Niu:
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation. 366-374 - Liwei Deng, Hao Sun, Yan Zhao, Shuncheng Liu, Kai Zheng:
S2TUL: A Semi-Supervised Framework for Trajectory-User Linking. 375-383 - Feifan Li, Lun Du, Qiang Fu, Shi Han, Yushu Du, Guangming Lu, Zi Li:
DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms. 384-392 - Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, Hao Wang:
Federated Unlearning for On-Device Recommendation. 393-401 - Qingyu Bing, Qiannan Zhu, Zhicheng Dou:
Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation. 402-410 - Xuewei Li, Aitong Sun, Mankun Zhao, Jian Yu, Kun Zhu, Di Jin, Mei Yu, Ruiguo Yu:
Multi-Intention Oriented Contrastive Learning for Sequential Recommendation. 411-419
Session 9: Language Models and Text Mining
- Noriaki Kawamae:
Friendly Conditional Text Generator. 420-428 - Yu Zhang, Yunyi Zhang, Martin Michalski, Yucheng Jiang, Yu Meng, Jiawei Han:
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts. 429-437 - Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, Jun Huang:
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning. 438-446 - Lilong Wen, Yingrong Wang, Dongxiang Zhang, Gang Chen:
Visual Matching is Enough for Scene Text Retrieval. 447-455 - Xiaodan Wang, Lei Li, Zhixu Li, Xuwu Wang, Xiangru Zhu, Chengyu Wang, Jun Huang, Yanghua Xiao:
AGREE: Aligning Cross-Modal Entities for Image-Text Retrieval Upon Vision-Language Pre-trained Models. 456-464 - Yuyan Chen, Zhixu Li, Jiaqing Liang, Yanghua Xiao, Bang Liu, Yunwen Chen:
Can Pre-trained Language Models Understand Chinese Humor? 465-480
Poster Session
- Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao:
IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation. 481-489 - Xiaoying Zhang, Hongning Wang, Hang Li:
Disentangled Representation for Diversified Recommendations. 490-498 - Yi Ren, Xiao Han, Xu Zhao, Shenzheng Zhang, Yan Zhang:
Slate-Aware Ranking for Recommendation. 499-507 - Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, Ming Zhang:
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation. 508-516 - Senrong Xu, Liangyue Li, Yuan Yao, Zulong Chen, Han Wu, Quan Lu, Hanghang Tong:
MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation. 517-525 - Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu, Han Xu:
VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation. 526-534 - Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, Zhiyuan Liu:
Knowledge-Adaptive Contrastive Learning for Recommendation. 535-543 - Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo:
Heterogeneous Graph Contrastive Learning for Recommendation. 544-552 - Zihao Li, Xianzhi Wang, Chao Yang, Lina Yao, Julian J. McAuley, Guandong Xu:
Exploiting Explicit and Implicit Item relationships for Session-based Recommendation. 553-561 - Jingkun Wang, Yongtao Jiang, Haochen Li, Wen Zhao:
Improving News Recommendation with Channel-Wise Dynamic Representations and Contrastive User Modeling. 562-570 - Himan Abdollahpouri, Zahra Nazari, Alex Gain, Clay Gibson, Maria Dimakopoulou, Jesse Anderton, Benjamin A. Carterette, Mounia Lalmas, Tony Jebara:
Calibrated Recommendations as a Minimum-Cost Flow Problem. 571-579 - Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, Maarten de Rijke:
Generative Slate Recommendation with Reinforcement Learning. 580-588 - Boyu Li, Ting Guo, Xingquan Zhu, Qian Li, Yang Wang, Fang Chen:
SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation. 589-597 - Chenxu Zhu, Bo Chen, Huifeng Guo, Hang Xu, Xiangyang Li, Xiangyu Zhao, Weinan Zhang, Yong Yu, Ruiming Tang:
AutoGen: An Automated Dynamic Model Generation Framework for Recommender System. 598-606 - Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou, Jintai Chen, Danny Z. Chen, Jian Wu:
Robust Training of Graph Neural Networks via Noise Governance. 607-615 - Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua:
Cooperative Explanations of Graph Neural Networks. 616-624 - Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu:
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection. 625-633 - Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang:
Towards Faithful and Consistent Explanations for Graph Neural Networks. 634-642 - Chang Liu, Yuwen Yang, Zhe Xie, Hongtao Lu, Yue Ding:
Position-Aware Subgraph Neural Networks with Data-Efficient Learning. 643-651 - Shenggui Tang, Kaixuan Yao, Jianqing Liang, Zhiqiang Wang, Jiye Liang:
Graph Neural Networks with Interlayer Feature Representation for Image Super-Resolution. 652-660 - Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang:
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation. 661-669 - Xiaowen Wei, Xiuwen Gong, Yibing Zhan, Bo Du, Yong Luo, Wenbin Hu:
CLNode: Curriculum Learning for Node Classification. 670-678 - Xin Zhou, Jinglong Wang, Yong Liu, Xingyu Wu, Zhiqi Shen, Cyril Leung:
Inductive Graph Transformer for Delivery Time Estimation. 679-687 - M. Eren Akbiyik, Mert Erkul, Killian Kämpf, Vaiva Vasiliauskaite, Nino Antulov-Fantulin:
Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data. 688-696 - Yan Han, Edward W. Huang, Wenqing Zheng, Nikhil Rao, Zhangyang Wang, Karthik Subbian:
Search Behavior Prediction: A Hypergraph Perspective. 697-705 - Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn:
A Multimodal Framework for the Identification of Vaccine Critical Memes on Twitter. 706-714 - Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, Ji-Rong Wen, Wayne Xin Zhao:
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation. 715-723 - Shuting Wang, Zhicheng Dou, Yutao Zhu:
Heterogeneous Graph-based Context-aware Document Ranking. 724-732 - Pritish Chakraborty, Sayan Ranu, Krishna Sri Ipsit Mantri, Abir De:
Learning and Maximizing Influence in Social Networks Under Capacity Constraints. 733-741 - Arpit Merchant, Michael Mathioudakis, Yanhao Wang:
Graph Summarization via Node Grouping: A Spectral Algorithm. 742-750 - Gongzhu Yin, Xing Wang, Hongli Zhang, Chao Meng, Yuchen Yang, Kun Lu, Yi Luo:
Beyond Individuals: Modeling Mutual and Multiple Interactions for Inductive Link Prediction between Groups. 751-759 - Lichao Sun, Xiaobin Rui, Wei Chen:
Scalable Adversarial Attack Algorithms on Influence Maximization. 760-768 - Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu:
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph. 769-777 - Linhao Luo, Gholamreza Haffari, Shirui Pan:
Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs. 778-786 - Qiaoyu Tan, Ninghao Liu, Xiao Huang, Soo-Hyun Choi, Li Li, Rui Chen, Xia Hu:
S2GAE: Self-Supervised Graph Autoencoders are Generalizable Learners with Graph Masking. 787-795 - Bing Liu, Tiancheng Lan, Wen Hua, Guido Zuccon:
Dependency-aware Self-training for Entity Alignment. 796-804 - Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu:
CL4CTR: A Contrastive Learning Framework for CTR Prediction. 805-813 - Wei Tang, Fenglong Su, Haifeng Sun, Qi Qi, Jingyu Wang, Shimin Tao, Hao Yang:
Weakly Supervised Entity Alignment with Positional Inspiration. 814-822 - Alex Deng, Lo-Hua Yuan, Naoya Kanai, Alexandre Salama-Manteau:
Zero to Hero: Exploiting Null Effects to Achieve Variance Reduction in Experiments with One-sided Triggering. 823-831 - Zhenran Xu, Zifei Shan, Yuxin Li, Baotian Hu, Bing Qin:
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark. 832-840 - Nihal Jain, Praneetha Vaddamanu, Paridhi Maheshwari, Vishwa Vinay, Kuldeep Kulkarni:
Self-supervised Multi-view Disentanglement for Expansion of Visual Collections. 841-849 - Thanh Trung Huynh, Minh Hieu Nguyen, Thanh Tam Nguyen, Phi Le Nguyen, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer:
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction. 850-858 - Xiaochuan Gou, Xiangliang Zhang:
Telecommunication Traffic Forecasting via Multi-task Learning. 859-867 - Yaguang Liu, Lisa Singh:
Combining vs. Transferring Knowledge: Investigating Strategies for Improving Demographic Inference in Low Resource Settings. 868-876 - Junnan Dong, Qinggang Zhang, Xiao Huang, Qiaoyu Tan, Daochen Zha, Zihao Zhao:
Active Ensemble Learning for Knowledge Graph Error Detection. 877-885 - Yasushi Kawase, Atsushi Miyauchi, Hanna Sumita:
Stochastic Solutions for Dense Subgraph Discovery in Multilayer Networks. 886-894 - Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Dawei Yin, Brian D. Davison:
Model-based Unbiased Learning to Rank. 895-903 - Xiaosu Wang, Yun Xiong, Beichen Kang, Yao Zhang, Philip S. Yu, Yangyong Zhu:
Reducing Negative Effects of the Biases of Language Models in Zero-Shot Setting. 904-912 - Haiyuan Zhao, Jun Xu, Xiao Zhang, Guohao Cai, Zhenhua Dong, Ji-Rong Wen:
Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking. 913-921 - Cheng Ji, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Philip S. Yu:
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning. 922-930 - Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto, Asia J. Biega:
Pairwise Fairness in Ranking as a Dissatisfaction Measure. 931-939 - Lequn Wang, Thorsten Joachims:
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems. 940-948 - Nikhita Vedula, Marcus D. Collins, Eugene Agichtein, Oleg Rokhlenko:
Generating Explainable Product Comparisons for Online Shopping. 949-957 - Xin Zhang, Jingling Yuan, Lin Li, Jianquan Liu:
Reducing the Bias of Visual Objects in Multimodal Named Entity Recognition. 958-966 - Aaron David Tucker, Thorsten Joachims:
Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits. 967-975 - Gang Chen, Jiawei Chen, Fuli Feng, Sheng Zhou, Xiangnan He:
Unbiased Knowledge Distillation for Recommendation. 976-984 - Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li:
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization. 985-993