We describe a novel, multi-task recommendation model, which jointly learns to perform
rating prediction and recommendation explanation by combining matrix factorization, for …

Despite widespread adoption, recommender systems remain mostly black boxes. Recently,
providing explanations about why items are recommended has attracted increasing …

Recommender systems have been playing an increasingly important role in our daily life
due to the explosive growth of information. Accuracy and explainability are two core aspects …

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

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 …

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

The increase in sophistication and complexity of recommendation algorithms has turned
them into black boxes where the algorithmic reasoning behind the predictions is hard to …

Explaining automatically generated recommendations allows users to make more informed
and accurate decisions about which results to utilize, and therefore improves their …

Recent studies have shown that both accuracy and explainability are important for
recommendation. In this paper, we introduce explainable conversational recommendation …

While collaborative filtering is the dominant technique in personalized recommendation, it
models user-item interactions only and cannot provide concrete reasons for a …