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 …

Propensity-based weighting methods have been widely studied and demonstrated
competitive performance in debiased recommendations. Nevertheless, there are still many …

In recommender systems, the collected data used for training is always subject to selection
bias, which poses a great challenge for unbiased learning. Previous studies proposed …

Ratings of a user to most items in recommender systems are usually missing not at random
(MNAR), largely because users are free to choose which items to rate. To achieve unbiased …

The collected data in recommender systems generally suffers selection bias. Considerable
works are proposed to address selection bias induced by observed user and item features …

Recommender systems rely on user-item interactions to learn Collaborative Filtering (CF)
signals and easily under-recommend the cold-start items without historical interactions. To …

Recommender systems are designed to learn user preferences from observed feedback and
comprise many fundamental tasks, such as rating prediction and post-click conversion rate …

In database of recommender systems, users' ratings for most items are usually missing,
resulting in selection bias when users selectively choose items to rate. To address this …

Trustworthy process monitoring seeks to build an accurate and interpretable monitoring
framework, which is critical for ensuring the safety of energy conversion plant (ECP) that …

Selection bias in recommender system arises from the recommendation process of system
filtering and the interactive process of user selection. Many previous studies have focused …