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

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

One way to increase trust among users towards recommender systems is to provide the
recommendation along with a textual explanation. In the literature, extraction-based …

Explanations in a recommender system assist users make informed decisions among a set
of recommended items. Extensive research attention has been devoted to generate natural …

As language models become increasingly integrated into our digital lives, Personalized Text
Generation (PTG) has emerged as a pivotal component with a wide range of applications …

Review information has been demonstrated beneficial for the explainable recommendation.
It can be treated as training corpora for generation-based methods or knowledge bases for …

Explaining to users why some items are recommended is critical, as it can help users to
make better decisions, increase their satisfaction, and gain their trust in recommender …

Providing natural language-based explanations to justify recommendations helps to improve
users' satisfaction and gain users' trust. However, as current explanation generation …

Explainable recommender systems can explain their recommendation decisions, enhancing
user trust in the systems. Most explainable recommender systems either rely on human …

Comparative recommendation explanations help to make sense of recommendations by
comparing a recommended item along some aspects of interest with one or many items …