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

In the area of electronic commerce, recommender systems have become more and more
popular. The quality of recommendations depends on the quality of the preference model …

Collaborative filtering (CF), particularly matrix factorization (MF) based methods, have been
widely used in recommender systems. The literature has reported that matrix factorization …

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 (CF) is the most classical method for recommender system, but it is
usually suffered from limited performance by the sparseness of user-to-item rating data …

Previous work on recommender systems mainly focus on fitting the ratings provided by
users. However, the response patterns, ie, some items are rated while others not, are …

Collaborative filtering recommendation with implicit feedbacks (eg, clicks, views, and plays)
is regarded as one of the most challenging issues in both academia and industry. From …

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 …

Collaborative filtering is one of the most popular techniques in designing recommendation
systems, and its most representative model, matrix factorization, has been wildly used by …

Nowadays, Recommendation system plays a vital role in industries like e-commerce, music
apps or newsgroup, retailers, etc. Broadly, recommender system techniques are categorized …