Recommender system (RS) suggests relevant objects to generate personalized service and
minimize information overload issue. User-based collaborative filtering (UBCF) plays a …

The recommender system (RS) can help us extract valuable data from a huge amount of raw
information. User-based collaborative filtering (UBCF) is widely employed in practical RSs …

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 …

Recommender systems, providing users with personalized recommendations from a
plethora of choices, have been an important component for e-commerce applications to …

Recommender systems (RS) analyze user rating information and recommend items that may
interest users. Item-based collaborative filtering (IBCF) is widely used in RSs. However …

Collaborative filtering (CF) has been a very successful approach for building recommender
system, but its widespread use has exposed to some well-known problems including …

Traditional collaborative filtering(CF) recommender systems (RS) suffer the problems of poor
rating accuracy, cold start and data sparsity. Friendship information is not used in current …

Recommender system (RS) has grown widely in various communities over the last few
years. It creates curiosity among the researchers due to the recent growth of various …

When information from traditional recommender systems is augmented with information
about user relationships that social networks store, more successful recommendations can …

Collaborative filtering is a popular recommendation algorithm. It predicts user's interests
according to the ratings or behaviour of other users in the system. However, the …