Welcome to the InfoSci Platform
Reference Hub1
Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems

Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems

Li Kuang, Gaofeng Cao, Liang Chen
Copyright: © 2017 |Volume: 14 |Issue: 2 |Pages: 23
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522511120|DOI: 10.4018/IJWSR.2017040101
Cite Article Cite Article

MLA

Kuang, Li, et al. "Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems." IJWSR vol.14, no.2 2017: pp.1-23. http://doi.org/10.4018/IJWSR.2017040101

APA

Kuang, L., Cao, G., & Chen, L. (2017). Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems. International Journal of Web Services Research (IJWSR), 14(2), 1-23. http://doi.org/10.4018/IJWSR.2017040101

Chicago

Kuang, Li, Gaofeng Cao, and Liang Chen. "Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems," International Journal of Web Services Research (IJWSR) 14, no.2: 1-23. http://doi.org/10.4018/IJWSR.2017040101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

As an effective way to solve information overload, recommender system has drawn attention of scholars from various fields. However, existing works mainly focus on improving the accuracy of recommendation by designing new algorithms, while the different importance of individual users has not been well addressed. In this paper, the authors propose new approaches to identifying core users based on trust relationships and interest similarity between users, and the popular degree, trust influence and resource of individual users. First, the trust degree and interest similarity between all user pairs, as well as the three attributes of individuals are calculated. Second, a global core user set is constructed based on three strategies, which are frequency-based, rank-based, and fusion-sorting-based. Finally, the authors compare their proposed methods with other existing methods from accuracy, novelty, long-tail distribution and user degree distribution. Experiments show the effectiveness of the authors' core user extraction methods.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.