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 …

Sparsity of rating data is a severe problem to be solved in modern recommendation
researches. The fusion recommendation method is an effective solution for the problem. The …

Inspired by the significant success of deep learning, some attempts have been made to
introduce deep neural networks (DNNs) in recommendation systems to learn users' …

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 …

Rich consumer online text data are embedded in the cloud platform. Using new
technologies has become a central issue for acquiring consumer preference, analyzing …

Recently, some attempts have been made in introducing deep neural networks (DNNs) to
recommender systems for generating more accurate prediction due to the nonlinear …

Personalized recommendation has become one of the most effective means to solve
information overload, and it is also a hot technology in the research field of massive data …

Traditional recommendation systems only consider the content of users to predict the rating
of items in the recommendation process, and ignore the impact of other factors on the …

In the recommendation domain, user rating data has high sparsity and the number of
interaction information from each user is very uneven, which brings great technical …

User basic data (eg user gender, user age and user ID, etc.) and item basic data (eg item
name, item category, etc.) are important side data that can be used to enhance the …