Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems.
Shengyu ZhangZiqi JiangJiangchao YaoFuli FengKun KuangZhou ZhaoShuo LiHongxia YangTat-Seng ChuaFei WuPublished in: IEEE Trans. Knowl. Data Eng. (2024)
Keyphrases
- recommender systems
- collaborative filtering
- information filtering
- causal relationships
- matrix factorization
- causal reasoning
- user profiles
- bayesian networks
- causal models
- causal inference
- information overload
- causal independence
- user profiling
- causal relations
- user interests
- causal discovery
- user preferences
- cold start problem
- trust aware
- causal networks
- cold start
- causal theories
- personalized recommendation
- user modeling
- causal structure
- implicit feedback
- causal knowledge
- product recommendation
- data sparsity
- user feedback
- semantic heterogeneity
- data sets
- relevance feedback
- probabilistic model
- netflix prize