Rating prediction is one of the key studies in the recommendation system. The traditional
rating prediction algorithms only utilize user's rating data to predict unknown ratings. In fact …

In a personalized product recommendation system, when the amount of log data is large or
sparse, the accuracy of model recommendation will be greatly affected. To solve this …

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

To solve the problem of data sparsity in recommendation systems, this paper proposes a
probabilistic matrix factorization recommendation of self-attention mechanism convolutional …

Personalized recommendation has become indispensable in today's information society.
Personalized recommendations play a significant role for both information producers and …

Big data has been developed for nearly a decade, and the information data on the network
is exploding. Facing the complex and massive data, it is difficult for people to get the …

Recommender systems recommend items more accurately by analyzing users' potential
interest on different brands' items. In conjunction with users' rating similarity, the presence of …

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

Recommender systems are recently becoming more significant in the age of rapid
development of the information technology and pervasive computing to provide e …

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 …