Latent factor models (LFMs) have been widely applied in many rating recommendation
systems because of their prediction rating capability. Nevertheless, LFMs may not fully …

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 …

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

Review rating prediction is an important research topic. The problem was approached from
either the perspective of recommender systems (RS) or that of sentiment analysis (SA) …

Personalized review-based rating prediction, a newly emerged research problem, aims at
inferring users' ratings over their unrated items using existing reviews and corresponding …

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful
way to learn graph data. The existing recommender systems based on the implicit factor …

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 …

It is common nowadays for e-commerce websites to encourage their users to rate shopping
items and write review text. This review text information has been proven to be very useful in …

Autoencoder-based rating prediction methods with external attributes have received wide
attention due to their ability to accurately capture users' preferences. However, existing …

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 …