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 …

Recommender systems may be confounded by various types of confounding factors (also
called confounders) that may lead to inaccurate recommendations and sacrificed …

Recommender systems easily face the issue of user preference shifts. User representations
will become out-of-date and lead to inappropriate recommendations if user preference has …

Recommender systems (RSs) have become an indispensable part of online platforms. With
the growing concerns of algorithmic fairness, RSs are not only expected to deliver high …

A fundamental challenge of recommender systems (RS) is understanding the causal
dynamics underlying users' decision making. Most existing literature addresses this problem …

In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to
capture comprehensive user preferences in order to ultimately enhance the …

Recommender Systems (RS) have significantly advanced online content discovery and
personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a …

Reciprocal recommender systems~(RRS), conducting bilateral recommendations between
two involved parties, have gained increasing attention for enhancing matching efficiency …

The first objective of recommender systems is to provide personalized recommendations for
each user. However, personalization may not be its only use. Past recommendations can be …

Abstract Machine learning has achieved significant success in many AI applications that
have been deployed. Most early approaches focused on optimizing performance …