Counterfactual explainable conversational recommendation
Conversational Recommender Systems (CRSs) fundamentally differ from traditional
recommender systems by interacting with users in a conversational session to accurately …
recommender systems by interacting with users in a conversational session to accurately …
Counterfactual explanation for fairness in recommendation
Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
Deconfounded recommendation via causal intervention
Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …
correlations between user/item profiles and user preference prediction, ie, the confounding …
Constrained off-policy learning over heterogeneous information for fairness-aware recommendation
Fairness-aware recommendation eliminates discrimination issues to build trustworthy
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …
[HTML][HTML] Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has emerged as a prominent method in recommendation
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …