Improving Tag-based Resource Recommendation with Association Rules on FolksonomiesReport as inadecuate

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1 Badji Mokhtar University 2 I3S - Laboratoire d-Informatique, Signaux, et Systèmes de Sophia Antipolis 3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems

Abstract : In this paper, we propose a method to analyze user profiles according to their tags in order to personalize the recommendation of resources. Our objective is to enrich the profiles of folksonomy users with pertinent resources. We argue that the automatic sharing of resources strengthens social links among actors and we exploit this idea to enrich user profiles by increasing the weights associated to web resources according to social relations. We base upon association rules which are a powerful method for discovering interesting relationships among a large set of data on the web. We extract association rules from folksonomies and use them to recommend supplementary resources associated to the tags involved in these rules. In this recommendation process, we reduce tag ambiguity by taking into account social similarities calculated on folksonomies.

Keywords : Folksonomies Social Tagging Association Rules Tag-based Resource Recommendation Tag Ambiguity

Author: Samia Beldjoudi - Hassina Seridi - Catherine Faron Zucker -



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