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Densifying a behavioral recommender system by social networks link prediction methods

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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.

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Acknowledgments

This research has been supported by the Credit Agricole Banking Group. We would like to thank M. Jean Philippe Blanchard for his collaboration and for providing the dataset experimented in this paper.

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Authors and Affiliations

  1. KIWI Team-LORIA, Nancy University, 615 rue du Jardin Botanique, 54600, Villers-Lès-Nancy, France

    Ilham Esslimani, Armelle Brun & Anne Boyer

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  1. Ilham Esslimani
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  2. Armelle Brun
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  3. Anne Boyer
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Correspondence to Ilham Esslimani.

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Esslimani, I., Brun, A. & Boyer, A. Densifying a behavioral recommender system by social networks link prediction methods. Soc. Netw. Anal. Min. 1, 159–172 (2011). https://doi.org/10.1007/s13278-010-0004-6

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  • DOI: https://doi.org/10.1007/s13278-010-0004-6

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