Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …

Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Recommender system is a specific type of intelligent systems, which exploits historical user
ratings on items and/or auxiliary information to make recommendations on items to the …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in
addressing collaborative filtering (CF) problems. During the MF process, the non-negativity …

Recommender systems are used to accurately and actively provide users with potentially
interesting information or services. Collaborative filtering is a widely adopted approach to …

Factorization approaches provide high accuracy in several important prediction problems,
for example, recommender systems. However, applying factorization approaches to a new …

With the emergence of online social networks, the social network-based recommendation
approach is popularly used. The major benefit of this approach is the ability of dealing with …

Nonnegative matrix factorization (NMF)-based models possess fine representativeness of a
target matrix, which is critically important in collaborative filtering (CF)-based recommender …

Social networks have become very important for networking, communications, and content
sharing. Social networking applications generate a huge amount of data on a daily basis …