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

By providing explanations for users and system designers to facilitate better understanding
and decision making, explainable recommendation has been an important research …

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 …

Recommender systems are gaining increasing and critical impacts on human and society
since a growing number of users use them for information seeking and decision making …

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 …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

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

To facilitate human decisions with credible suggestions, personalized recommender
systems should have the ability to generate corresponding explanations while making …

Although the applications of artificial intelligence especially deep learning have greatly
improved various aspects of intelligent manufacturing, they still face challenges for broader …

As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …