Paper Info
Title
Densifying a behavioral recommender system by social networks link prediction methods
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.
Year
DOI
Venue
2011
10.1007/s13278-010-0004-6
Social Netw. Analys. Mining
Keywords
Field
DocType
recommender systemusage analysis � behavioral networkssocial networkslink prediction,collaborative filtering,recommender system,information retrieval system,social network
Recommender system,Data mining,Collaborative filtering,Usage analysis,Social network,Computer science,Exploit,Artificial intelligence,Machine learning,Personalization
Journal
Volume
Issue
ISSN
1
3
1869-5469
Citations 
PageRank 
References 
19
0.84
30
Authors
3
Name
Order
Citations
PageRank
Ilham Esslimani1402.66
Armelle Brun213821.49
Anne Boyer310618.08