The emergence of Pre-trained Language Models (PLMs) has achieved tremendous success
in the field of Natural Language Processing (NLP) by learning universal representations on …

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 …

Recent years have witnessed the wide adoption of large language models (LLM) in different
fields, especially natural language processing and computer vision. Such a trend can also …

Computer Vision (CV), Natural Language Processing (NLP), and Recommender Systems
(RecSys) are three prominent AI applications that have traditionally developed …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

Foundation Models such as Large Language Models (LLMs) have significantly advanced
many research areas. In particular, LLMs offer significant advantages for recommender …

Pre-trained language models (PLMs) have demonstrated strong performance in sequential
recommendation (SR), which are utilized to extract general knowledge. However, existing …

Causal reasoning and logical reasoning are two important types of reasoning abilities for
human intelligence. However, their relationship has not been extensively explored under …

Recent models can generate fluent and grammatical synthetic reviews while accurately
predicting user ratings. The generated reviews, expressing users' estimated opinions …

Explanations in conventional recommender systems have demonstrated benefits in helping
the user understand the rationality of the recommendations and improving the system's …