Collaborative filtering inherently suffers from the data sparsity and cold start problems.
Social networks have been shown useful to help alleviate these issues. However, social …

Matrix factorization is one of the most successful model-based collaborative filtering
approaches in recommender systems. Nevertheless, useful latent user features can lead to …

The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that
the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF) …

Matrix approximation is a common model-based approach to collaborative filtering in
recommender systems. Many relevant algorithms that fuse social contextual information …

Collaborative filtering is the most popular approach for recommender systems. One way to
perform collaborative filtering is matrix factorization, which characterizes user preferences …

User ratings and tags are becoming largely available on Internet. While people usually
exploit user ratings for developing recommender systems, the use of tag information in …

Recommendation techniques play a vital role in recommending an actual product to an
intended user. The recommendation also supports the user in the decision-making process …

Collaborative filtering is one of the most common scenarios and popular research topics in
recommender systems. Among existing methods, latent factor models, ie, learning a specific …

Collaborative filtering is a fundamental technique in recommender systems, for which
memory-based and matrix-factorization-based collaborative filtering are the two types of …

Recommender systems that predict the preference of users have attracted more and more
attention in decades. One of the most popular methods in this field is collaborative filtering …