A Semantic Similarity Measure for Recommender Systems
| Autor: | Yolaine Bourda, Géraldine Polaillon, Roza Lemdani, Nacéra Bennacer |
|---|---|
| Přispěvatelé: | Faivre, Evelyne, Supélec Sciences des Systèmes (E3S), Ecole Supérieure d'Electricité - SUPELEC (FRANCE) |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Information retrieval
[INFO.INFO-WB] Computer Science [cs]/Web Computer science business.industry [INFO.INFO-WB]Computer Science [cs]/Web 02 engineering and technology Social Semantic Web Semantic grid Semantic similarity 020204 information systems Semantic computing 0202 electrical engineering electronic engineering information engineering Semantic analytics Semantic technology 020201 artificial intelligence & image processing Semantic integration Semantic Web Stack business |
| Zdroj: | Proceedings of the 7th International Conference on Semantic Systems-I-SEMANTIC 2011 7th International Conference on Semantic Systems-I-SEMANTIC 2011 7th International Conference on Semantic Systems-I-SEMANTIC 2011, Sep 2011, Graz, Austria. pp.183-186 I-SEMANTICS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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