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From Social Networks to Behavioral Networks in Recommender Systems

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 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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https://hal.inria.fr/inria-00395679
Contributor : Armelle Brun <>
Submitted on : Tuesday, June 16, 2009 - 11:30:03 AM
Last modification on : Tuesday, April 24, 2018 - 1:37:38 PM

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  • HAL Id : inria-00395679, version 1

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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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