In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Recently, large language models (LLMs)(eg, GPT-4) have demonstrated impressive general-
purpose task-solving abilities, including the potential to approach recommendation tasks …

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

Most of the existing GNN-based recommender system models focus on learning users'
personalized preferences from these (explicit/implicit) positive feedback to achieve …

Session-based Recommendations recommend the next possible item for the user with
anonymous sessions, whose challenge is that the user's behavioral preference can only be …

Cross-domain recommendation (CDR) aims at leveraging the correlation of users' behaviors
in both the source and target domains to improve the user preference modeling in the target …

In recommender systems, top-N recommendation is an important task with implicit feedback
data. Although the recent success of deep learning largely pushes forward the research on …

Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and
higher-order interactions for recommender systems. However, compared with traditional …

Collaborative filtering (CF) is widely used to learn informative latent representations of users
and items from observed interactions. Existing CF-based methods commonly adopt negative …