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

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

Large language models (LLM) have manifested unparalleled modeling capability on various
tasks, eg, multi-step reasoning, but the input to these models is mostly limited to plain text …

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 …

Existing research on fairness-aware recommendation has mainly focused on the
quantification of fairness and the development of fair recommendation models, neither of …

There is increasing recognition of the need for human-centered AI that learns from human
feedback. However, most current AI systems focus more on the model design, but less on …

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

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

Modern recommender systems are increasingly expected to provide informative
explanations that enable users to understand the reason for particular recommendations …