• Corpus ID: 245769824

Deep Causal Reasoning for Recommendations

@inproceedings{Zhu2022DeepCR,
  title={Deep Causal Reasoning for Recommendations},
  author={Yaochen Zhu and Jing Yi and Jiayi Xie and Zhenzhong Chen},
  year={2022}
}
Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that a ff ect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, a new trend in recommender system research is to negate the influence of confounders from a causal perspective. Observing that confounders in recommendations are usually shared among items and… 

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