Détection de communautés d'intérêt et recommandation sociale par leaders
Identifieur interne : 000087 ( PascalFrancis/Corpus ); précédent : 000086; suivant : 000088Détection de communautés d'intérêt et recommandation sociale par leaders
Auteurs : Armelle Brun ; Anne BoyerSource :
- Ingénierie des systèmes d'information : (2001) [ 1633-1311 ] ; 2012.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Recommender Systems aim at increasing the users' satisfaction in an online service, by suggesting items that correspond to their preferences. In this article we aim at increasing the quality of the recommendations, as well as reducing the size of the recommendation model. To increase the quality of recommendations, we propose a new way to form communities of interest, by exploiting the ratio between the similarity within communities and the similarity outside of the communities. To reduce the size of the recommendation model, we propose to select a subset of users, the leaders. We show that the new algorithm used to build communities leads to an improvement of the recommendations and that the leader-based recommender decreases the size of the model by 80%, while maintaining a high coverage and accuracy.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 13-0100266 INIST |
---|---|
FT : | Détection de communautés d'intérêt et recommandation sociale par leaders |
FT : | (Identification of communities of interest and leader-based social recommendations) |
AU : | BRUN (Armelle); BOYER (Anne); CABANAC (Guillaume); CHEVALIER (Max); MOTHE (Josiane) |
AF : | LORIA - Nancy Université 615, rue du jardin botanique/54506 Vandœuvre-lès-Nancy/France (1 aut., 2 aut.); IRIT, CNRS, Université de Toulouse/Toulouse/France (1 aut., 2 aut., 3 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Ingénierie des systèmes d'information : (2001); ISSN 1633-1311; France; Da. 2012; Vol. 17; No. 6; Pp. 91-113; Abs. anglais; Bibl. 2 p.3/4 |
LA : | Français |
EA : | Recommender Systems aim at increasing the users' satisfaction in an online service, by suggesting items that correspond to their preferences. In this article we aim at increasing the quality of the recommendations, as well as reducing the size of the recommendation model. To increase the quality of recommendations, we propose a new way to form communities of interest, by exploiting the ratio between the similarity within communities and the similarity outside of the communities. To reduce the size of the recommendation model, we propose to select a subset of users, the leaders. We show that the new algorithm used to build communities leads to an improvement of the recommendations and that the leader-based recommender decreases the size of the model by 80%, while maintaining a high coverage and accuracy. |
CC : | 001D02B04 |
FD : | Service web; Recommandation; Satisfaction; Préférence; Réseau social; Précision élevée; Intérêt; Modélisation; Organisation sociale; . |
ED : | Web service; Recommendation; Satisfaction; Preference; Social network; High precision; Interest; Modeling; Social organization |
SD : | Servicio web; Recomendación; Satisfacción; Preferencia; Red social; Precisión elevada; Interés; Modelización; Organización social |
LO : | INIST-26729.354000506318200040 |
ID : | 13-0100266 |
Links to Exploration step
Pascal:13-0100266Le document en format XML
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<front><div type="abstract" xml:lang="en">Recommender Systems aim at increasing the users' satisfaction in an online service, by suggesting items that correspond to their preferences. In this article we aim at increasing the quality of the recommendations, as well as reducing the size of the recommendation model. To increase the quality of recommendations, we propose a new way to form communities of interest, by exploiting the ratio between the similarity within communities and the similarity outside of the communities. To reduce the size of the recommendation model, we propose to select a subset of users, the leaders. We show that the new algorithm used to build communities leads to an improvement of the recommendations and that the leader-based recommender decreases the size of the model by 80%, while maintaining a high coverage and accuracy.</div>
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<FT>Détection de communautés d'intérêt et recommandation sociale par leaders</FT>
<FT>(Identification of communities of interest and leader-based social recommendations)</FT>
<AU>BRUN (Armelle); BOYER (Anne); CABANAC (Guillaume); CHEVALIER (Max); MOTHE (Josiane)</AU>
<AF>LORIA - Nancy Université 615, rue du jardin botanique/54506 Vandœuvre-lès-Nancy/France (1 aut., 2 aut.); IRIT, CNRS, Université de Toulouse/Toulouse/France (1 aut., 2 aut., 3 aut.)</AF>
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<EA>Recommender Systems aim at increasing the users' satisfaction in an online service, by suggesting items that correspond to their preferences. In this article we aim at increasing the quality of the recommendations, as well as reducing the size of the recommendation model. To increase the quality of recommendations, we propose a new way to form communities of interest, by exploiting the ratio between the similarity within communities and the similarity outside of the communities. To reduce the size of the recommendation model, we propose to select a subset of users, the leaders. We show that the new algorithm used to build communities leads to an improvement of the recommendations and that the leader-based recommender decreases the size of the model by 80%, while maintaining a high coverage and accuracy.</EA>
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