Improving Tag-based Resource Recommendation with Association Rules on Folksonomies

Samia Beldjoudi 1 Hassina Seridi 1 Catherine Faron Zucker 2, 3
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - 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.
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Samia Beldjoudi, Hassina Seridi, Catherine Faron Zucker. Improving Tag-based Resource Recommendation with Association Rules on Folksonomies. 2nd ISWC Workshop on Semantic Personalized Information Management: Retrieval and Recommendation, SPIM 2011, 2011, Bonn, Germany. ⟨hal-01201744⟩

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