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 …

Recommender systems are promising for providing personalized favorite services.
Collaborative filtering (CF) technologies, making prediction of users' preference based on …

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

Recently, matrix factorization has produced state-of-the-art results in recommender systems.
However, given the typical sparsity of ratings, the often large problem scale, and the large …

Matrix Factorization (MF) techniques have already shown its strong foundation in
collaborative filtering (CF), particularly for rating prediction problem. In the basic MF model …

In real-world recommender systems, some users are easily influenced by new products and
whereas others are unwilling to change their minds. So the preference varying speeds for …

Collaborative Filtering (CF) is a popular way to build recommender systems and has been
successfully employed in many applications. Generally, two kinds of approaches to CF, the …

One of the most popular approaches to Collaborative Filtering is based on Matrix
Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy …

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 …

Most collaborative filtering algorithms are based on certain statistical models of user
interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks …