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A Semantic Similarity Measure for Recommender Systems

Abstract : In the past few years, recommender systems and semantic web technologies have become main subjects of interest in the research community. In this paper, we present a domain independent semantic similarity measure that can be used in the recommendation process. This semantic similarity is based on the relations between the individuals of an ontology. The assessment can be done offline which allows time to be saved and then, get real-time recommendations. The measure has been experimented on two different domains: movies and research papers. Moreover, the generated recommendations by the semantic similarity have been evaluated by a set of volunteers and the results have been promising.
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Conference papers
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https://hal-supelec.archives-ouvertes.fr/hal-00625585
Contributor : Evelyne Faivre <>
Submitted on : Thursday, September 22, 2011 - 9:16:25 AM
Last modification on : Wednesday, June 20, 2018 - 2:32:02 PM

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  • HAL Id : hal-00625585, version 1

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Roza Lemdani, Géraldine Polaillon, Nacéra Bennacer Seghouani, Yolaine Bourda. A Semantic Similarity Measure for Recommender Systems. 7th International Conference on Semantic Systems - I-SEMANTIC 2011, Sep 2011, Graz, Austria. pp.183-186. ⟨hal-00625585⟩

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