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Conference Papers Year : 2009

From Social Networks to Behavioral Networks in Recommender Systems

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Ilham Esslimani
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  • PersonId : 855366
Armelle Brun
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  • PersonId : 831057
Anne Boyer

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 systems use only direct links and common features to model relationships between users. This paper presents a new Collaborative Filtering approach (BNCF) based on a behavioral network that uses navigational patterns to model relationships between users and exploits social networks techniques, such as transitivity, to explore additional links throughout the behavioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. BNCF is evaluated in terms of accuracy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions. Indeed, BNCF highly improves the accuracy of predictions, especially in terms of HMAE.
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Dates and versions

inria-00395679 , version 1 (16-06-2009)

Identifiers

  • HAL Id : inria-00395679 , version 1

Cite

Ilham Esslimani, Armelle Brun, Anne Boyer. From Social Networks to Behavioral Networks in Recommender Systems. The 2009 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Jul 2009, Athènes, Greece. ⟨inria-00395679⟩
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