Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

Sequential Cross-Domain Recommendation (CDR) has been popularly studied to utilize
different domain knowledge and users' historical behaviors for the next-item prediction. In …

Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by
modeling how users transit among items. However, the short interaction sequences limit the …

With the development of modern internet techniques, Cross-Domain Recommendation
(CDR) systems have been widely exploited for tackling the data-sparsity problem …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Session-based Recommendation aims at predicting the next interacted item based on short
anonymous behavior sessions. However, existing solutions neglect to model two inherent …

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 …

Cross-Domain Recommendation (CDR) aims to solve the data sparsity problem by
integrating the strengths of different domains. Though researchers have proposed various …

With the growing privacy concerns in recommender systems, recommendation unlearning is
getting increasing attention. Existing studies predominantly use training data, ie, model …