Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Densifying a behavioral recommender system by social networks link prediction methods

Densifying a behavioral recommender system by social networks link prediction methods Recommender systems are widely used for personalization of information on the Web and information retrieval systems. collaborative filtering (CF) is the most popular recommendation technique. However, classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new densified behavioral network based collaborative filtering model (D-BNCF), based on the BNCF approach that uses navigational patterns to model relationships between users. D-BNCF exploits additionally social networks techniques, such as prediction link methods, to discover new links throughout the behavioral network. The final aim is the involvement of these new links in prediction generation to improve the quality of recommendations. The approach proposed is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions as a high precision is reached. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows also the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Network Analysis and Mining Springer Journals

Densifying a behavioral recommender system by social networks link prediction methods

Loading next page...
 
/lp/springer-journals/densifying-a-behavioral-recommender-system-by-social-networks-link-vSyNCDnoi0
Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Computer Science; Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Methodology of the Social Sciences
ISSN
1869-5450
eISSN
1869-5469
DOI
10.1007/s13278-010-0004-6
Publisher site
See Article on Publisher Site

Abstract

Recommender systems are widely used for personalization of information on the Web and information retrieval systems. collaborative filtering (CF) is the most popular recommendation technique. However, classical CF (CCF) systems use only direct links and common features to model relationships between users. This paper presents a new densified behavioral network based collaborative filtering model (D-BNCF), based on the BNCF approach that uses navigational patterns to model relationships between users. D-BNCF exploits additionally social networks techniques, such as prediction link methods, to discover new links throughout the behavioral network. The final aim is the involvement of these new links in prediction generation to improve the quality of recommendations. The approach proposed is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions as a high precision is reached. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows also the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions.

Journal

Social Network Analysis and MiningSpringer Journals

Published: Aug 28, 2010

References