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

Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

Recommendation accuracy is a fundamental problem in the quality of the recommendation
system. In this article, we propose an efficient deep matrix factorization (EDMF) with review …

Combining with music recommendations that may be received through mobile devices, in
comparison to past periods, the usage of digital music has risen in recent years. Searching …

Fashion recommendation has attracted increasing attention from both industry and
academic communities. This paper proposes a novel neural architecture for fashion …

Recently, user cold-start recommendations have attracted a lot of attention from industry and
academia. In user cold-start recommendation systems, the user attribute information is often …

In a large recommender system, the products (or items) could be in many different
categories or domains. Given two relevant domains (eg, Book and Movie), users may have …

In recent years, the use of digital music is increased compare to earlier period. Based on the
selected features of music or songs, the music recommendation service provider will provide …

Despite the great success of many matrix factorization based collaborative filtering
approaches, there is still much space for improvement in recommender system field. One …

Providing explanations in a recommender system is getting more and more attention in both
industry and research communities. Most existing explainable recommender models regard …