A collaborative filtering approach combining clustering and navigational based correlations

Ilham Esslimani 1 Armelle Brun 1, * Anne Boyer 1
* Auteur correspondant
1 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Recommender systems are widely used for automatic personalization of information on web sites and informa- tion retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique, but several CF systems still suffer from problems like data rating availability and space dimensionality for neighborhood selection. In this paper, we present a new CF approach (PSN-CF) that uses usage traces to model users. These traces are used to estimate ratings that will be employed to generate clusters. Then, the PSN-CF evaluates navigational correlations between users within these clusters. Predictions are performed in a following step. The performance of PSN-CF is evaluated in terms of accuracy and time processing on a real usage dataset. We show that PSN-CF highly improves the accuracy of predictions in terms of MAE. Moreover, the use of clustering and positive sequences before computing the navigational correlations contributes to an important reduction of time processing.
Type de document :
Communication dans un congrès
5th International Conference on Web Information Systems and Technologies - WEBIST 2009, Mar 2009, Lisbonne, Portugal. pp.364-369, 2009
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https://hal.inria.fr/inria-00395573
Contributeur : Armelle Brun <>
Soumis le : lundi 15 juin 2009 - 18:41:16
Dernière modification le : mardi 24 avril 2018 - 13:37:37

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  • HAL Id : inria-00395573, version 1

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Ilham Esslimani, Armelle Brun, Anne Boyer. A collaborative filtering approach combining clustering and navigational based correlations. 5th International Conference on Web Information Systems and Technologies - WEBIST 2009, Mar 2009, Lisbonne, Portugal. pp.364-369, 2009. 〈inria-00395573〉

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