基于因果推断的推荐系统.pdf

1、基于因果推断的推荐系统高宸清华大学 信息国家研究中心https:/ 2023:因果推断在线峰会推荐与因果推断论坛Background2 2 Why is causal inference needed in recommender system?Chen Gao et al.Causal inference in recommender systems:A survey and future directionsJ.arXiv preprint arXiv:2208.12397,2022.Outline3 3 Disentangled learning for user interest an

2、d conformity Disentangled learning for long-term and short-term interests Debiasing in short-video recommendationDisentangling User Interest and Conformity for Recommendation with Causal EmbeddingY.Zheng,Chen Gao,et al.Disentangling user interest and conformity for recommendation with causal embeddi

3、ngC/Proceedings of the Web Conference 2021.2021:2980-2991.4Background5 5 What are the causes behind each user-item interaction?There are two main causes:InterestConformitya best-sellerbuybuyhigh salestire,speed,.How users tend to follow other peopleGoal:Learn disentangled representations for interes

4、t and conformityMotivation6 6 Why learning disentangled representations?Causal recommendation under non-IID situations!IID:independent and identically distributed Robustness Recommenders are trained and updated in real-time Training data and test data are not IID Interpretability Improve user-friend

5、liness Facilitates algorithm developingtraining datatest datarepresentationCausal Recommendation7 7 Inverse Propensity Scoring(IPS)1propensityscore Propensity score is estimated from item popularity Intuition:impose lower weights on popular items,andboost unpopular items Interest and popularity are

6、bundled as one unifiedrepresentationTwo factors are entangled!1 Yang,L.,Cui,Y.,Xuan,Y.,Wang,C.,Belongie,S.,&Estrin,D.(2018,September).Unbiased offline recommender evaluation for missing-not-at-random implicit feedback.In Proceedings of the 12th ACM Conference on Recommender Systems(pp.279-287).Causa