Most existing collaborative filtering techniques have focused on modeling the binary relation
of users to items by extracting from user ratings. Aside from users' ratings, their affiliated …

In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …

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 …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Due to the flexibility in modelling data heterogeneity, heterogeneous information network
(HIN) has been adopted to characterize complex and heterogeneous auxiliary data in …

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 …

A large amount of information exists in reviews written by users. This source of information
has been ignored by most of the current recommender systems while it can potentially …

Recently, many e-commerce websites have encouraged their users to rate shop** items
and write review texts. This review information has been very useful for understanding user …

Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality
of recommender system. To handle the sparsity problem, several recommendation …

Heterogeneous Information Network (HIN) is a natural and general representation of data in
modern large commercial recommender systems which involve heterogeneous types of …