With the rapid growth of information, recommender systems have become integral for
providing personalized suggestions and overcoming information overload. However, their …

Graph Neural Network (GNN) based social recommendation models improve the prediction
accuracy of user preference by leveraging GNN in exploiting preference similarity contained …

Neural graph collaborative filtering has received great recent attention due to its power of
encoding the high-order neighborhood via the backbone graph neural networks. However …

Knowledge graph (KG) plays an increasingly important role to improve the recommendation
performance and interpretability. A recent technical trend is to design end-to-end models …

Recommender systems have become an important instrument to connect people to
information. Sparse, complex, and rapidly growing data presents new challenges to …

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy
of users when transferring the knowledge from source domain to target domain for better …

As its availability and generality in online services, implicit feedback is more commonly used
in recommender systems. However, implicit feedback usually presents noisy samples in real …

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

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 …

In recommender systems, click behaviors play a fundamental role in mining users' interests
and training models (clicked items as positive samples). Such signals are implicit feedback …