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 …

Recently, research on explainable recommender systems has drawn much attention from
both academia and industry, resulting in a variety of explainable models. As a consequence …

Explanations in recommender systems help users better understand why a recommendation
(or a list of recommendations) is generated. Explaining recommendations has become an …

This research proposes a novel strategy for constructing a knowledge-based recommender
system (RS) based on both structured data and unstructured text data. We present its …

Explainable recommendation is a technique that combines prediction and generation tasks
to produce more persuasive results. Among these tasks, textual generation demands large …

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 …

Explainable recommender system has recently drawn increasing attention due to its
capability of providing justification to recommendation. Rather than focusing on certain …

Reciprocal recommender systems (RRS) have been widely used in online platforms such as
online dating and recruitment. They can simultaneously fulfill the needs of both parties …

While contextual data plays an important role for the outputs generated by AI models, it has
not been fully considered when providing explanations about how and why those models …