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Recommendation Systems (RS) are often plagued by popularity bias.
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How Graph Convolutions Amplify Popularity Bias for Recommendation?
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On Measuring Popularity Bias in Collaborative Filtering Data. In Proceedings of the Workshops of the EDBT/ICDT 2020 Joint Conference, Copenhagen, Denmark, March 30, 2020 , Vol. 2578
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Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, and Xiangnan He. 2023c · 2023
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Adap- τ \tau : Adaptively Modulating Embedding Magnitude for Recommendation. In Proceedings of the ACM Web Conference 2023 . 1085–1096
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Gradient descent for deep matrix factorization: Dynamics and implicit bias towards low rank
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CIRS: Bursting filter bubbles by counterfactual interactive recommender system
Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, and Peng Jiang. 2023b · 2023
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Test Time Embedding Normalization for Popularity Bias Mitigation
Dain Kim, Jinhyeok Park, and Dongwoo Kim. 2023 · 2023
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Balancing unobserved confounding with a few unbiased ratings in debiased recommendations. In Proceedings of the ACM Web Conference 2023 . 1305–1313
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Yuanhao Liu, Qi Cao, Huawei Shen, Yunfan Wu, Shuchang Tao, and Xueqi Cheng. 2023 · 2023
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Curse of "Low" Dimensionality in Recommender Systems. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 537–547
Naoto Ohsaka and Riku Togashi. 2023 · 2023
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XSimGCL: Towards extremely simple graph contrastive learning for recommendation
Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2023 · 2023
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Invariant Collaborative Filtering to Popularity Distribution Shift. In Proceedings of the ACM Web Conference 2023 . 1240–1251
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A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1760–1764
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Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering
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Macro graph neural networks for online billion-scale recommender systems. In Proceedings of the ACM on Web Conference 2024 . 3598–3608
Hao Chen, Yuanchen Bei, Qijie Shen, Yue Xu, Sheng Zhou, Wenbing Huang, Feiran Huang, Senzhang Wang, and Xiao Huang. 2024a · 2024
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How graph convolutions amplify popularity bias for recommendation?
Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, and Xiangnan He. 2024b · 2024
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InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization. In Proceedings of the ACM on Web Conference 2024 . 1350–1361
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ReCRec: Reasoning the Causes of Implicit Feedback for Debiased Recommendation
Siyi Lin, Sheng Zhou, Jiawei Chen, Yan Feng, Qihao Shi, Chun Chen, Ying Li, and Can Wang. 2024 · 2024
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Debiasing Recommendation with Personal Popularity. In Proceedings of the ACM on Web Conference 2024 . 3400–3409
Wentao Ning, Reynold Cheng, Xiao Yan, Ben Kao, Nan Huo, Nur Al Hasan Haldar, and Bo Tang. 2024 · 2024
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Distributionally Robust Graph-based Recommendation System. In Proceedings of the ACM on Web Conference 2024 . 3777–3788
Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, and Can Wang. 2024a · 2024
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Causally Debiased Time-aware Recommendation. In Proceedings of the ACM on Web Conference 2024 . 3331–3342
Lei Wang, Chen Ma, Xian Wu, Zhaopeng Qiu, Yefeng Zheng, and Xu Chen. 2024b · 2024
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On the Effectiveness of Sampled Softmax Loss for Item Recommendation
Jiancan Wu, Xiang Wang, Xingyu Gao, Jiawei Chen, Hongcheng Fu, Tianyu Qiu, and Xiangnan He. 2024 · 2024
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General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout. In Proceedings of the ACM on Web Conference 2024 . 3864–3875
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