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 …

摘要互联网技术的迅猛发展把我们带进了信息爆炸的时代. 海量信息的同时呈现,
一方面使用户很难从中发现自己感兴趣的部分, 另一方面也使得大量少人问津的信息成为网络中 …

Recently, recommender systems are widely used on various platforms in real world to
provide personalized recommendations. However, sparsity is a tough problem in a …

Recommender systems have been in existence everywhere with most of them using single
ratings in prediction. However, multi-criteria predictions have been proved to be more …

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

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 …

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 …

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 …

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