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

Hybrid recommender systems combine several different sources of information to generate
recommendations. These systems demonstrate improved accuracy compared to single …

Collaborative filtering recommendation algorithms generate suggestions based on similar
interactions between users. Although it provides accurate recommendations, the approach …

Recommender systems are ubiquitous and shape the way users access information and
make decisions. As these systems become more complex, there is a growing need for …

Explanations can give credibility to recommendations and help users to make better
choices. In current recommender systems, explanation is a step that comes after …

Studies have shown that there is an intimate connection between the process of computing
recommendations and the process of generating corresponding explanations and that this …

As part of our motivation to advance societal acceptance of and trust in explainable artificial
intelligence (XAI)—namely, explainable case-based reasoning (XCBR) systems—we …

Hybrid recommender systems (RS) have shown to improve system accuracy by combining
benefits from the collaborative filtering (CF) and content-based (CB) approaches. Recently …

Recommender systems are discovery tools. Typically, they infer a user's preferences from
her behaviour and make personalized suggestions. They are one response to the …

The World Wide Web (WWW) has grown quickly in the past two decades from a small
research community to the biggest and most popular infrastructure for communication …