Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Recently, machine/deep learning techniques are achieving remarkable success in a variety
of intelligent control and management systems, promising to change the future of artificial …

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem
results in the selection bias issue, degrading the recommendation performance ultimately. A …

In recommender systems, some features directly affect whether an interaction would
happen, making the happened interactions not necessarily indicate user preference. For …

User intent modeling in natural language processing deciphers user requests to allow for
personalized responses. The substantial volume of research (exceeding 13,000 …

Conversational Recommender Systems (CRSs) fundamentally differ from traditional
recommender systems by interacting with users in a conversational session to accurately …

Duration bias widely exists in video recommendations, where models tend to recommend
short videos for the higher ratio of finish playing and thus possibly fail to capture users' true …

Multi-modal fake news detection has drawn considerable attention with the development of
online social media. Existing methods primarily conduct direct cross-modal fusion, while …

Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …

Traditional recommenders suffer from hidden confounding factors, leading to the spurious
correlations between user/item profiles and user preference prediction, ie, the confounding …