Enhancing a navigational based recommender system by link prediction methods

Ilham Esslimani 1 Armelle Brun 1, * Anne Boyer 1
* Auteur correspondant
1 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Recommender systems are widely used for personalization of information on the web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommen- dation technique. However, Classical CF (CCF) systems use only direct links and common features to model relationships between users. This report presents a Behavioral Network Collaborative Filtering approach (BNCF) that uses navi- gational patterns to model relationships between users and exploits social networks techniques, such as prediction link methods, to explore additional links throughout the behav- ioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. The proposed approach is evaluated in terms of ac- curacy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions in terms of HMAE. Besides, the evaluation of a combined model (that exploits both best accurate BNCF and CCF based models) shows the importance of combining similari- ties of two different networks and its impact on accuracy of high predictions.
Type de document :
[Intern report] 2009
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Contributeur : Armelle Brun <>
Soumis le : lundi 15 juin 2009 - 18:35:11
Dernière modification le : mardi 24 avril 2018 - 13:37:27


  • HAL Id : inria-00395571, version 1



Ilham Esslimani, Armelle Brun, Anne Boyer. Enhancing a navigational based recommender system by link prediction methods. [Intern report] 2009. 〈inria-00395571〉



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