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 …

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 …

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 …

In the traditional recommendation algorithms, due to the rapid development of deep learning
and Internet technology, user-item rating data is becoming increasingly sparse. The simple …

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 …

Due to the expansion of e-business, the availability of products on the internet has massively
increased. Finding suitable stuff from the vast array of products available on the internet is a …

Collaborative filtering is one of the most commonly used methods in recommendation
systems. However, the sparsity of the rating matrix, cold start-up, and most recommendation …

A recommender system plays a vital role in information filtering and retrieval, and its
application is omnipresent in many domains. There are some drawbacks such as the cold …

The collaborative filtering adopted by traditional recommendation system has data sparsity
problem, and the matrix decomposition method simply decomposes users and items into …

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 …