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Deconfounded Causal Collaborative Filtering [article]

Shuyuan Xu and Juntao Tan and Shelby Heinecke and Jia Li and Yongfeng Zhang
2021 arXiv   pre-print
Experiments on real-world e-commerce datasets show that our method is able to deconfound unobserved confounders to achieve better recommendation performance.  ...  Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance.  ...  Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors.  ... 
arXiv:2110.07122v1 fatcat:2ytfb42y6bcndopzvs4gp3455m

Causal Intervention for Leveraging Popularity Bias in Recommendation [article]

Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang
2021 pre-print
To achieve our goal, we propose a new training and inference paradigm for recommendation named Popularity-bias Deconfounding and Adjusting (PDA).  ...  Empirical studies validate that the deconfounded training is helpful to discover user real interests and the inference adjustment with popularity bias could further improve the recommendation accuracy.  ...  We claim that PD can alleviate the amplification of popularity bias, i.e., removing the bad effect of popularity bias.  ... 
doi:10.1145/3404835.3462875 arXiv:2105.06067v1 fatcat:osksijktdjevfli7ibjw6jlc3i