Recommender systems (RS) play a critical role in delivering personalized content across
various online platforms, leveraging collaborative filtering (CF) as a key technique to …

Numerous social recommendation models leverage user-item rating data (ie, collaborative
domain) and social connections (ie, social domain) to obtain users' preferences for …

The interaction data used for training recommender systems often exhibit a long-tail
distribution. Such highly imbalanced data distribution results in an unfair learning process …

Graph contrastive learning has been widely used in recommender systems to extract
meaningful representations by analyzing the similarities and differences between data …

Abstract Graph Neural Networks (GNNs) are susceptible to inheriting and even amplifying
biases within datasets, subsequently leading to discriminatory decision-making. Our …

Explainable recommender systems, which aim to provide accurate recommendations and
reliable explanations, have attracted significant research interest due to their ability to …