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

Implicit feedback collaborative filtering has attracted a lot of attention in collaborative
filtering, which is called one-class collaborative filtering (OCCF). However, the low …

The Matrix-Factorization (MF) based models have become popular when building
Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability …

Recommender systems are software tools and techniques for suggesting items in an
automated fashion to users tailored their preferences. Collaborative Filtering (CF) …

Collaborative filtering (CF) approaches generate user recommendations based on user
similarities. These similarities are calculated based on the overall (explicit) user ratings …

Recommender systems play an important role in providing personalized services for users
in the context of information overload. Generally, users' feedback toward items often contain …

Abstract Context-aware Collaborative Filtering (CF) techniques such as Factorization
Machines (FM) have been proven to yield high precision for rating prediction. However, the …

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 (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) is one of the most effective techniques in recommender systems,
which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms …