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

Recently, deep learning based techniques exploiting reviews are extensively studied for
rating prediction and result in good performance. Some studies consider word level review …

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 …

Reviews information is dominant for users to make online purchasing decisions in e-
commerces. However, the usefulness of reviews is varied. We argue that less-useful reviews …

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 …

Although latent factor models (eg, matrix factorization) achieve good accuracy in rating
prediction, they suffer from several problems including cold-start, non-transparency, and …

The Web has accumulated a rich source of information, such as text, image, rating, etc,
which represent different aspects of user preferences. However, the heterogeneous nature …

Personalized rating prediction is an important research problem in recommender systems.
Although the latent factor model (eg, matrix factorization) achieves good accuracy in rating …

Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is available …