Densifying a Behavioral Recommender System by Social Networks link prediction methods

Ilham Esslimani 1 Armelle Brun 1 Anne Boyer 1
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 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 in terms of HMAE. Besides, the evaluation of a combined model (that exploits the more accurate D-BNCF models) shows the interest of combining similarities based on two different link prediction methods and its impact on the accuracy of high predictions.
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Article dans une revue
Social Network Analysis and Mining, Springer, 2011, 1 (3), pp.159--172. 〈10.1007/s13278-010-0004-6〉
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https://hal.inria.fr/inria-00430331
Contributeur : Armelle Brun <>
Soumis le : vendredi 6 novembre 2009 - 14:54:25
Dernière modification le : mardi 24 avril 2018 - 13:54:15

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Ilham Esslimani, Armelle Brun, Anne Boyer. Densifying a Behavioral Recommender System by Social Networks link prediction methods. Social Network Analysis and Mining, Springer, 2011, 1 (3), pp.159--172. 〈10.1007/s13278-010-0004-6〉. 〈inria-00430331〉

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