Abstract Traditional Collaborative Filtering (CF) models mainly focus on predicting a user's
preference to the items in a single domain such as the movie domain or the music domain. A …

Collaborative filtering (CF) is a widely adopted technique in recommender systems.
Traditional CF models mainly focus on predicting the user preference to items in a single …

Collaborative filtering (CF) is a major technique in recommender systems to help users find
their potentially desired items. Since the data sparsity problem is quite commonly …

Sparsity is a tough problem in a single domain collaborative filtering (CF) recommendation
system as it is difficult to compute the similarities among users accurately. Recently, cross …

Recently, various cross-domain recommendation (CDR) models are proposed to overcome
the sparsity problem, which leverage relatively abundant rating data from the auxiliary …

Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-
item preference data. In many real-world applications, preference data are usually sparse …

Collaborative Filtering (CF) is one of the most successful recommendation approaches to
cope with information overload in the real world. However, typical CF methods equally treat …

Abstract Recently, Cross Domain Collaborate Filtering (CDCF) is a new way to alleviate the
sparsity problem in the recommender systems. CDCF solves the sparsity problem by …

Traditional recommendation algorithms such as matrix factorization, collaborative filtering
perform poorly when lack of interactive information of user and product, known as the user …

Due to the ability of addressing the data sparsity and cold-start problems, Cross-Domain
Collaborative Filtering (CDCF) has received a significant amount of attention. Despite …