The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Multi-task learning (MTL) has been successfully applied to many recommendation
applications. However, MTL models often suffer from performance degeneration with …

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

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

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

Recommender systems have been widely applied to several domains and applications to
assist decision making by recommending items tailored to user preferences. One of the …

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 …

Personalized recommender systems are important to assist user decision-making in the era
of information overload. Meanwhile, explanations of the recommendations further help users …

Knowledge graphs have been widely adopted to improve recommendation accuracy. The
multi-hop user-item connections on knowledge graphs also endow reasoning about why an …