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 …

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

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

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

Recently, the embedding-based recommendation models (eg, matrix factorization and deep
models) have been prevalent in both academia and industry due to their effectiveness and …

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

In recent years, explainable recommendation has been a topic of active study. This is
because the branch of the machine learning field related to methodologies is enabling …

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

Explainable recommendation, which provides explanations about why an item is
recommended, has attracted increasing attention due to its ability in helping users make …

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