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Recommender system usually faces popularity bias issues: from the data perspective, items exhibit uneven (long-tail) distribution on the interaction frequency; from the method perspective, collaborative filtering methods are prone to amplify the bias by over-recommending popular items.
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How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In Proceedings of the 12th ACM Conference on Recommender Systems . 224–232
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Modeling Users’ Exposure with Social Knowledge Influence and Consumption Influence for Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management . 953–962
Jiawei Chen, Yan Feng, Martin Ester, Sheng Zhou, Chun Chen, and Can Wang. 2018 · 2018
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Harald Steck. 2018 · 2018
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Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019 · 2019
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Estimating Position Bias without Intrusive Interventions. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . 474–482
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, and Thorsten Joachims. 2019 · 2019
Cited alongside, same era.
Local Popularity and Time in Top-N Recommendation. In European Conference on Information Retrieval . 861–868
Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, and Joseph Trotta. 2019 · 2019
Cited alongside, same era.
Offline Evaluation to Make Decisions about Playlist Recommendation Algorithms. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining . 420–428
Alois Gruson, Praveen Chandar, Christophe Charbuillet, James McInerney, Samantha Hansen, Damien Tardieu, and Ben Carterette. 2019 · 2019
Cited alongside, same era.
Real-Time Attention Based Look-Alike Model for Recommender System. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2765–2773
Yudan Liu, Kaikai Ge, Xu Zhang, and Leyu Lin. 2019 · 2019
Cited alongside, same era.
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A Re-visit of the Popularity Baseline in Recommender Systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 1749–1752
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2020 · 2020
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Modeling Popularity and Temporal Drift of Music Genre Preferences
Elisabeth Lex, Dominik Kowald, and Markus Schedl. 2020 · 2020
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A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 831–840
Dugang Liu, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming. 2020 · 2020
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Feedback Loop and Bias Amplification in Recommender Systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2145–2148
Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2020 · 2020
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Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions. In Fourteenth ACM Conference on Recommender Systems . 687–691
Rishabh Mehrotra, Prasanta Bhattacharya, and Mounia Lalmas. 2020 · 2020
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Unbiased Learning for the Causal Effect of Recommendation. In Fourteenth ACM Conference on Recommender Systems . 378–387
Masahiro Sato, Sho Takemori, Janmajay Singh, and Tomoko Ohkuma. 2020 · 2020
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Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020
Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, and Yefeng Zheng. 2020a · 2020
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Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning. In Proceedings of The Web Conference 2020 . 2775–2781
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu, Keping Yang, Quan Lin, Hong Wen, and Ramin Ramezani. 2020 · 2020
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Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. In Fourteenth ACM Conference on Recommender Systems . 551–556
Ziwei Zhu, Yun He, Yin Zhang, and James Caverlee. 2020 · 2020
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Disentangling User Interest and Conformity Bias for Recommendation with Causal Embedding. In Proceedings of the Web Conference 2021
Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, and Yong Li. 2021 · 2021
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Popularity-Opportunity Bias in Collaborative Filtering. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 85–93
Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021 · 2021
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