Recommender systems aim to provide users with preferred items to address the information
overload problem in the Web era. Social relations, item connections, and user-generated …

The performance of a recommendation system relies heavily on the feedback of users. Most
of the traditional recommendation algorithms based only on historical ratings will encounter …

Recommender systems aim to estimate item ratings and recommend items based on the
users' interests. The traditional recommender systems generally consider user–item rating …

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

Most of the existing recommender systems are based only on the rating data, and they
ignore other sources of information that might increase the quality of recommendations, such …

The problem of personalized review-based rating prediction aims at inferring users' ratings
over their unrated items using historical ratings and reviews. Most of existing methods solve …

Rating prediction, whose goal is to predict user preference for unconsumed items, has
become one of the core tasks in recommendation systems. Recently, many deep learning …

Most of the existing recommender systems are based only on the rating data, and they
ignore other sources of information that might increase the quality of recommendations, such …

In the era of information explosion, recommender systems are widely studied and applied to
explore users' preferences. Reviews often reflect users' real thoughts and play an important …

Recommender systems are widely deployed in Web applications that need to predict the
preferences of users to items. They are popular in helping users find movies, books, music …