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

As one of the most successful approaches to building recommender systems, collaborative
filtering (CF) uses the known preferences of a group of users to make recommendations or …

This paper presents an overview of the field of recommender systems and describes the
current generation of recommendation methods that are usually classified into the following …

Although Recommender Systems have been comprehensively analyzed in the past decade,
the study of social-based recommender systems just started. In this paper, aiming at …

Data sparsity, scalability and prediction quality have been recognized as the three most
crucial challenges that every collaborative filtering algorithm or recommender system …

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

With increasing presence and adoption of Web services on the World Wide Web, Quality-of-
Service (QoS) is becoming important for describing nonfunctional characteristics of Web …

As an indispensable technique in the field of Information Filtering, Recommender System
has been well studied and developed both in academia and in industry recently. However …

Memory-based methods for collaborative filtering predict new ratings by averaging
(weighted) ratings between, respectively, pairs of similar users or items. In practice, a large …

Recommender systems are becoming increasingly important to individual users and
businesses for providing personalized recommendations. However, while the majority of …