Deep learning-based recommendations have demonstrated impressive performance in
improving recommendation accuracy. However, such approaches mainly utilize implicit …

Collaborative filtering (CF) is a central solution for capturing various user-item relationships
in building recommender systems. However, when the relationships are sparsely observed …

The integration of artificial intelligence (AI) into daily life, particularly through information
retrieval and recommender systems, has necessitated advanced user modeling and …

Cross-domain recommendation (CDR) has become popular to alleviate the sparsity problem
in target-domain recommendation by utilizing auxiliary domain knowledge. A basic …

Learning dynamic user preference has become an increasingly important component for
many online platforms (eg, video-sharing sites, e-commerce systems) to make sequential …

Multi-behavioral recommender systems have emerged as a solution to address data sparsity
and cold-start issues by incorporating auxiliary behaviors alongside target behaviors …

Multi-behavior recommendations effectively integrate various types of behaviors and have
been proven to enhance recommendation performance. However, existing researches …

Traditional recommendations typically prioritize modeling the target user's one type of
behavior while ignoring other auxiliary behaviors, resulting in low recommendation …

Micro-video prediction with the multi-behavior sequence remains a challenging task for
current recommendation systems. Existing approaches tend to model each individual …

Multi-behavior recommender systems (MBRS) have been commonly deployed on real-world
industrial platforms for their superior advantages in understanding user preferences and …