Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Explainable recommendation attempts to develop models that generate not only high-quality
recommendations but also intuitive explanations. The explanations may either be post-hoc …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …

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

Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …

To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

Recent advances in personalized recommendation have sparked great interest in the
exploitation of rich structured information provided by knowledge graphs. Unlike most …

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

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 …