For a long time, different recommendation tasks require designing task-specific architectures
and training objectives. As a result, it is hard to transfer the knowledge and representations …

Recommendation systems have witnessed significant advancements and have been widely
used over the past decades. However, most traditional recommendation methods are task …

The debut of ChatGPT has recently attracted significant attention from the natural language
processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT …

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

The learn-to-compare paradigm of contrastive representation learning (CRL), which
compares positive samples with negative ones for representation learning, has achieved …

Collaborative filtering (CF) plays a critical role in the development of recommender systems.
Most CF methods utilize an encoder to embed users and items into the same representation …

Collaborative filtering (CF) models easily suffer from popularity bias, which makes
recommendation deviate from users' actual preferences. However, most current debiasing …

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …

Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …