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In the era of information overload, recommender systems (RSs) have become an indispensable part of online service platforms.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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Direct and indirect effects
Judea Pearl · 2001
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Probabilistic matrix factorization
Andriy Mnih and Russ R Salakhutdinov · 2007
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Collaborative filtering and the missing at random assumption
Benjamin M Marlin, Richard S Zemel, Sam Roweis, and Malcolm Slaney · 2007
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Collaborative filtering for implicit feedback datasets
Yifan Hu, Yehuda Koren, and Chris Volinsky · 2008
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Factorization meets the neighborhood: A multifaceted collaborative filtering model
Yehuda Koren · 2008
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Causality
Judea Pearl · 2009
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Probabilistic graphical models: Principles and techniques
Daphne Koller and Nir Friedman · 2009
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BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2009
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Collaborative prediction and ranking with non-random missing data
Benjamin M Marlin and Richard S Zemel · 2009
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Personalized news recommendation based on click behavior
Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen · 2010
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Factorization machines
Steffen Rendle · 2010
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Training and testing of recommender systems on data missing not at random
Harald Steck · 2010
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Machine learned job recommendation
Ioannis Paparrizos, B Barla Cambazoglu, and Aristides Gionis · 2011
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Collaborative topic modeling for recommending scientific articles
Chong Wang and David M Blei · 2011
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Exploiting geographical influence for collaborative point-of-interest recommendation
Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee · 2011
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Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira · 2011
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Content-based recommender systems: State of the art and trends
Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro · 2011
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Item popularity and recommendation accuracy
Harald Steck · 2011
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Evaluating recommendation systems
Guy Shani and Asela Gunawardana · 2011
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A contextual-bandit algorithm for mobile context-aware recommender system
Djallel Bouneffouf, Amel Bouzeghoub, and Alda Lopes Gançarski · 2012
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Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality
Thomas S Richardson and James M Robins · 2013
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Counterfactual reasoning and learning systems: The example of computational advertising
Léon Bottou, Jonas Peters, Joaquin Quiñonero-Candela, Denis X Charles, D Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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The MovieLens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2015
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Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
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Estimating the causal impact of recommendation systems from observational data
Amit Sharma, Jake M Hofman, and Duncan J Watts · 2015
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Scalable recommendation with hierarchical poisson factorization
Prem Gopalan, Jake M Hofman, and David M Blei · 2015
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Hashtag recommendation using attention-based convolutional neural network
Yuyun Gong and Qi Zhang · 2016
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Collaborative denoising auto-encoders for top-N recommender systems
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester · 2016
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Recommendations as treatments: Debiasing learning and evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims · 2016
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Causal inference for recommendation
Dawen Liang, Laurent Charlin, and David M Blei · 2016
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Hybrid recommender systems: A systematic literature review
Erion Çano and Maurizio Morisio · 2017
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Deep matrix factorization models for recommender systems
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen · 2017
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Neural factorization machines for sparse predictive analytics
Xiangnan He and Tat-Seng Chua · 2017
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Controlling popularity bias in learning-to-rank recommendation
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher · 2017
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The book of why: The new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
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Variational autoencoders for collaborative filtering
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara · 2018
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Position bias estimation for unbiased learning to rank in personal search
Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork · 2018
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Causal embeddings for recommendation
Stephen Bonner and Flavian Vasile · 2018
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Unbiased learning to rank with unbiased propensity estimation
Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, and W Bruce Croft · 2018
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Exploiting POI-specific geographical influence for point-of-interest recommendation
Hao Wang, Huawei Shen, Wentao Ouyang, and Xueqi Cheng · 2018
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He · 2021
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Autodebias: Learning to debias for recommendation
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang · 2021
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Deconfounded causal collaborative filtering
Shuyuan Xu, Juntao Tan, Shelby Heinecke, Jia Li, and Yongfeng Zhang · 2021
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Disentangling user interest and conformity for recommendation with causal embedding
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Yong Li, and Depeng Jin · 2021
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Contrastive learning for debiased candidate generation in large-scale recommender systems
Chang Zhou, Jianxin Ma, Jianwei Zhang, Jingren Zhou, and Hongxia Yang · 2021
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Deep interest network for click-through rate prediction
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai · 2018
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A large scale benchmark for uplift modeling
Eustache Diemert, Artem Betlei, Christophe Renaudin, and Massih-Reza Amini · 2018
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Deep learning based recommender system: A survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay · 2019
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MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video
Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua · 2019
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Uplift-based evaluation and optimization of recommenders
Masahiro Sato, Janmajay Singh, Sho Takemori, Takashi Sonoda, Qian Zhang, and Tomoko Ohkuma · 2019
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A general framework for counterfactual learning-to-rank
Aman Agarwal, Kenta Takatsu, Ivan Zaitsev, and Thorsten Joachims · 2019
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Multi-cause effect estimation with disentangled confounder representation
Jing Ma, Ruocheng Guo, Aidong Zhang, and Jundong Li · 2021
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Popularity bias in dynamic recommendation
Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee · 2021
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User-oriented fairness in recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2021
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Towards personalized fairness based on causal notion
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang · 2021
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Counterfactual explainable recommendation
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang · 2021
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Learning causal explanations for recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, and Yongfeng Zhang · 2021
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CausalVAE: Disentangled representation learning via neural structural causal models
Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, and Jun Wang · 2021
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Advances in collaborative filtering
Yehuda Koren, Steffen Rendle, and Robert Bell · 2022
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Causal inference in recommender systems: A survey and future directions
Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, and Yong Li · 2022
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Mutually-regularized dual collaborative variational auto-encoder for recommendation systems
Yaochen Zhu and Zhenzhong Chen · 2022
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Variational bandwidth auto-encoder for hybrid recommender systems
Yaochen Zhu and Zhenzhong Chen · 2022
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Causal factorization machine for robust recommendation
Yunqi Li, Hanxiong Chen, Juntao Tan, and Yongfeng Zhang · 2022
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On the opportunity of causal learning in recommendation systems: Foundation, estimation, prediction and challenges
Peng Wu, Haoxuan Li, Yuhao Deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, and Xiao-Hua Zhou · 2022
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Dynamic causal collaborative filtering
Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, and Yongfeng Zhang · 2022
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Mitigating hidden confounding effects for causal recommendation
Xinyuan Zhu, Yang Zhang, Fuli Feng, Xun Yang, Dingxian Wang, and Xiangnan He · 2022
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Causal recommendation: Progresses and future directions. Tutorial for The Web Conference 2022
Yang Zhang, Wenjie Wang, Peng Wu, Fuli Feng, and Xiangnan He · 2022
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Unbiased sequential recommendation with latent confounders
Zhenlei Wang, Shiqi Shen, Zhipeng Wang, Bo Chen, Xu Chen, and Ji-Rong Wen · 2022
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Deep causal reasoning for recommendations
Yaochen Zhu, Jing Yi, Jiayi Xie, and Zhenzhong Chen · 2022
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Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, and Wei Wu · 2022
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Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders
Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao, and Binqiang Zhao · 2022
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Fairness in graph mining: A survey
Yushun Dong, Jing Ma, Chen Chen, and Jundong Li · 2022
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Fairness in recommendation: A survey
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang · 2022
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Causal disentanglement for semantics-aware intent learning in recommendation
Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, and Guandong Xu · 2022
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Causal disentanglement with network information for debiased recommendations
Paras Sheth, Ruocheng Guo, Kaize Ding, Lu Cheng, K Selçuk Candan, and Huan Liu · 2022
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Deep deconfounded content-based tag recommendation for UGC with causal intervention
Yaochen Zhu, Xubin Ren, Jing Yi, and Zhenzhong Chen · 2022
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Debiased cross-modal matching for content-based micro-video background music recommendation
Jing Yi and Zhenzhong Chen · 2022
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KuaiRec: A fully-observed dataset and insights for evaluating recommender systems
Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, and Tat-Seng Chua · 2022
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Causal representation learning for out-of-distribution recommendation
Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua · 2022
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Learning causality with graphs
Jing Ma and Jundong Li · 2022
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Be causal: De-biasing social network confounding in recommendation
Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu · 2022
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Learning causal effects on hypergraphs
Jing Ma, Mengting Wan, Longqi Yang, Jundong Li, Brent Hecht, and Jaime Teevan · 2022
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