To alleviate the sparsity issue, many recommender systems have been proposed to
consider the review text as the auxiliary information to improve the recommendation quality …

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

User-generated product reviews contain a lot of valuable information including users'
opinions on products and product features that is not fully exploited by the current …

Rating-based methods (eg, collaborative filtering) in recommendation can explicitly model
users and items from their rating patterns, nevertheless suffer from the natural data sparsity …

For many years user textual reviews have been exploited to model user/item representations
for enhancing the performance of the Recommender System (RS). However, the traditional …

Textual reviews contain fine-grained information that can effectively infer user preferences
over the items. Accordingly, the latest studies in the field of recommender systems exploit …

Recent studies in recommender systems have managed to achieve significantly improved
performance. However, despite being extensively studied, these methods still suffer from two …

Two-tower neural networks are popularly used in review-aware recommender systems, in
which two encoders are separately employed to learn representations for users and items …

The existing review-based recommendation methods usually employ the same model to
learn the review representation of users and items. However, for different user-item pairs, the …

With a sharp improvement in E-commerce and data, the precise rating prediction of
recommended items under user preferences has been a hot research topic in the EC …