Filtrage collaboratif sensible au contexte

Filtrage collaboratif sensible au contexte

Josiane Mothe Ambinintsoa Jocelyn Rakotonirina 

ESPE, Université de Toulouse, Université de Toulouse Jean Jaurès IRIT, UMR 5505 CNRS, 118 Route de Narbonne, Toulouse, France

DMI, Université d’Antananarivo, Madagascar Mathématiques Informatique et Statistique Appliquées, MISA BP 906 Ankatso

Corresponding Author Email:;
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Recommender systems are designed to provide user with items related to their ongoing browsing and that may be of interest to them. User interest depends on the context. In this work, we propose a hybrid CBCF (Context-aware Based Collaborative Filtering) system combining context-sensitive and collaborative filtering. We define context as the objective or intent of the user. We model it by a LDA (Latent Dirichlet Allocation) approach which generates a topic model for each intention. We evaluated our approach using the Book-Crossing collection and demonstrated the superiority of our model over several state-of-the-art methods.


information systems, information retrieval, recommender systems, latent dirichlet allocation, collaborative filtering, hybrid recommender system

1. Introduction
2. État de l’art
3. Filtrage collaboratif basé sur LDA
4. Evaluation et résultats
5. Conclusion

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