Providing user-understandable explanations to justify recommendations could help users
better understand the recommended items, increase the system's ease of use, and gain …

Personalization of natural language generation plays a vital role in a large spectrum of
tasks, such as explainable recommendation, review summarization and dialog systems. In …

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 …

Personalized recommender systems are important to assist user decision-making in the era
of information overload. Meanwhile, explanations of the recommendations further help users …

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

Understanding user preference is of key importance for an effective recommender system.
For comprehensive user profiling, many efforts have been devoted to extract user feature …

Generating recommendation reasons for recommendation results is a long-standing
problem because it is challenging to explain the underlying reasons for recommending an …

Explainable recommendations have drawn more attention from both academia and industry
recently, because they can help users better understand recommendations (ie, why some …

Deep neural networks have been serving as the main driving force for the emergence of
cutting-edge applications in many areas including computer vision, speech recognition …

Most of the current machine learning approaches to IR---including search and
recommendation tasks---are mostly designed based on the basic idea of matching, which …