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

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

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 …

User-based collaborative filtering (UBCF) is widely used in recommender systems (RSs) as
one of the most successful approaches, but traditional UBCF cannot provide …

Collaborative filtering (CF) is typically used for recommending those items to a user which
other like-minded users preferred in the past. User-based collaborative filtering (UbCF) and …

Recently, Deep Neural Networks (DNNs) have been largely utilized in Collaborative
Filtering (CF) to produce more accurate recommendation results due to their ability of …

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 …

Collaborative filtering has been widely used in many applications. The typical idea is to
identify preferences of users by utilizing their interaction data over the whole items …

Abstract One-Class Collaborative Filtering (OCCF) has recently received much attention in
recommendation communities due to their close relationship with real industry problem …