Clustering based recommender systems have been demonstrated to be efficient and
scalable to large-scale datasets. However, due to the employment of dimensionality …

With the booming of online social networks, social trust has been used to cluster users in
recommender systems. It has been proven to improve the recommendation accuracy when …

Collaborative filtering is one of the widely used technologies in the e-commerce
recommender systems. It can predict the interests of a user based on the rating information …

With the development of personalized recommendations, information overload has been
alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of …

Collaborative filtering (CF) is one of the most well-known and commonly used techniques to
build recommender systems and generate recommendations. However, it suffers from …

Collaborative filtering suffers from the problems of data sparsity and cold start, which
dramatically degrade recommendation performance. To help resolve these issues, we …

The ever-increasing popularity of recommendation systems allows users to find appropriate
services without excessive effort. However, due to the unstable and complex network …

Using trust relationships can improve the accuracy of recommendation systems; however, it
is affected by data sparsity. Random walks can harvest the behavioral relationships between …

Providing high quality recommendations is important for e-commerce systems to assist users
in making effective selection decisions from a plethora of choices. Collaborative filtering is a …

Recommender systems using traditional collaborative filtering suffer from some significant
weaknesses, such as data sparseness and scalability. In this study, we propose a method …