In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

Recommender systems, which provide users with recommendations of content suited to
their needs, have received great attention in today's online business world. However, most …

In today's society, time is considered more valuable than money, and researchers often have
limited time to find relevant papers for their research. Identifying and accessing essential …

Today, people frequently communicate through interactions and exchange knowledge over
the social web in various formats. Social connections have been substantially improved by …

The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …

Collaborative filtering (CF) is a widely used approach in recommender systems to solve
many real-world problems. Traditional CF-based methods employ the user-item matrix …

The collaborative filtering (CF) technique has been widely utilized in recommendation
systems due to the precise prediction of users' interests. Most prior CF methods adapted …

Collaborative filtering (CF) has become one of the most popular and widely used methods in
recommender systems, but its performance degrades sharply for users with rare interaction …

With the rapid development and application of the mobile Internet, huge amounts of user
data are generated and collected every day. How to take full advantages of these ubiquitous …

Recommender Systems deal with the issue of overloading information by retrieving the most
relevant sources in the wide range of web services. They help users by predicting their …