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 …

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 …

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 …

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 …

Recommender system has recently attracted a lot of attention in the information service
community. Currently, most recommendation models use deep neural networks to learn user …

Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …

These days, many recommender systems (RS) are utilized for solving information overload
problem in areas such as e-commerce, entertainment, and social media. Although classical …

With the advent of web technology, user-generated textual reviews are becoming
increasingly accumulated on many e-commerce websites. These reviews contain not only …

Both reviews and user-item interactions (ie, rating scores) have been widely adopted for
user rating prediction. However, these existing techniques mainly extract the latent …

With the fast development of online E-commerce Websites and mobile applications, users'
auxiliary information as well as products' textual information can be easily collected to form a …