With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Existing works in recommender system have widely explored extracting reviews as
explanations beyond user–item interactions, and formulated the explanation generation as a …

There have been excellent results using knowledge graphs in recommender systems.
Knowledge graphs can be used as auxiliary information to alleviate data sparsity and …

Personalized recommendations for social tagging systems aim to deliver high-quality
recommendations for users and annotate meaningful characteristics on items. However …

To provide precise recommendations, traditional recommender systems (RS) collect
personal data, user preference and feedback, which are sensitive to each user if such …

Recently, one critical issue looms large in the field of recommender systems–there are no
effective benchmarks for rigorous evaluation–which consequently leads to unreproducible …

In recent practices, sparsity problems often arise in recommendation systems, resulting in
weak generalization ability. To alleviate this problem, tag-aware recommendation systems …

Last year has witnessed the re-flourishment of tag-aware recommender systems supported
by the LLM-enriched tags. Unfortunately, though large efforts have been made, current …

Accurately predicting individual-level infection state is of great value since its essential role
in reducing the damage of the epidemic. However, there exists an inescapable risk of …

Dynamic graph networks are widely used for learning time-evolving graphs, but prior work
on training these networks is inefficient due to communication overhead, long …