The recommendation system makes recommendations based on the preferences of the
users. These user preferences usually come from the user's basic information, item rating …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

A knowledge graph (KG) consists of a set of interconnected typed entities and their
attributes. Recently, KGs are popularly used as the auxiliary information to enable more …

Collaborative Filtering (CF) recommender models highly depend on user-item interactions to
learn CF representations, thus falling short of recommending cold-start items. To address …

Sequential recommendation (SR) learns from the temporal dynamics of user-item
interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of …

Incorporating knowledge graphs (KGs) as side information in recommendation has recently
attracted considerable attention. Despite the success in general recommendation scenarios …

Personalized recommender systems aim to predict users' preferences for items. It has
become an indispensable part of online services. Online social platforms enable users to …

With the emergence of online music platforms, music recommender systems are becoming
increasingly crucial in music information retrieval. Knowledge graphs (KGs) are a rich …

In recommender system, the proportion of interacted items with users is extremely sparse
compared to the total number of items. This data sparsity problem particularly affects …

A knowledge graph (KG) contains a large amount of well-structured external triple
information that can effectively solve the problems of poor interpretability in collaborative …