Users leave many reviews while participating in network activities, and these have been
proven to improve the performance of recommendation systems. However, most current …

It is beneficial to use user review as a preference expression because they contain
information that is not in the interaction record. However, most current research on …

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 …

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 …

Collaborative filtering has been successful in the recommendation systems of various
scenarios, but it is also hampered by issues such as cold start and data sparsity. To alleviate …

Reviews and user–item interactions have been widely used to predict the behaviors of
users. However, the sparsity of user–item interactions on datasets remains a major …

User and item reviews are valuable for the construction of recommender systems. In
general, existing review-based methods for recommendation can be broadly categorized …

In rating prediction research, how to capture user and item features from review text is a key
to improving model prediction accuracy. The sparsity of review text and the accuracy of the …

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 …

With the development of e-commerce platforms, user reviews have become a vital source of
information to address the sparsity problems for enhancing the predictive performance of the …