Exploitation du skipping pour la modélisation prédictive des usages du web: Vers une meilleure prise en compte du bruit
Identifieur interne : 000089 ( PascalFrancis/Corpus ); précédent : 000088; suivant : 000090Exploitation du skipping pour la modélisation prédictive des usages du web: Vers une meilleure prise en compte du bruit
Auteurs : Geoffray Bonnin ; Armelle Brun ; Anne BoyerSource :
- Revue d'intelligence artificielle [ 0992-499X ] ; 2012.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Predictive web usage modeling has undergone an intense period of investigation until the late 90's. However, two features of web browsing have rarely been taken into account: the presence of noise and parallel browsing. In this paper, we propose a new model, the SBR model (Skipping-Based Recommender) which uses a technique called skipping, and is able to take into account these features. In a series of experimental studies, we put forward the various contributions that this model possesses and show that its quality surpasses that of the state-of-the-art.
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Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 13-0093408 INIST |
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FT : | Exploitation du skipping pour la modélisation prédictive des usages du web: Vers une meilleure prise en compte du bruit |
ET : | (Using skipping for predictive web usage modeling. Towards a better robustness to noise) |
AU : | BONNIN (Geoffray); BRUN (Armelle); BOYER (Anne) |
AF : | LORIA - Équipe KIWI Campus Scientifique - BP 239/54506 Vandoeuvre-lès-Nancy/France (1 aut., 2 aut., 3 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2012; Vol. 26; No. 6; Pp. 609-642; Abs. anglais; Bibl. 2 p.1/4 |
LA : | Français |
EA : | Predictive web usage modeling has undergone an intense period of investigation until the late 90's. However, two features of web browsing have rarely been taken into account: the presence of noise and parallel browsing. In this paper, we propose a new model, the SBR model (Skipping-Based Recommender) which uses a technique called skipping, and is able to take into account these features. In a series of experimental studies, we put forward the various contributions that this model possesses and show that its quality surpasses that of the state-of-the-art. |
CC : | 001D02B04; 001D02B07B |
FD : | Internet; Navigation information; Analyse donnée; Fouille donnée; Comportement utilisateur; Réseau web; Recommandation; Modélisation; Immunité bruit; Modèle Markov; . |
ED : | Internet; Information browsing; Data analysis; Data mining; User behavior; World wide web; Recommendation; Modeling; Noise immunity; Markov model |
SD : | Internet; Navegacíon informacíon; Análisis datos; Busca dato; Comportamiento usuario; Red WWW; Recomendación; Modelización; Inmunidad ruido; Modelo Markov |
LO : | INIST-21320.354000506317880010 |
ID : | 13-0093408 |
Links to Exploration step
Pascal:13-0093408Le document en format XML
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<FT>Exploitation du skipping pour la modélisation prédictive des usages du web: Vers une meilleure prise en compte du bruit</FT>
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<EA>Predictive web usage modeling has undergone an intense period of investigation until the late 90's. However, two features of web browsing have rarely been taken into account: the presence of noise and parallel browsing. In this paper, we propose a new model, the SBR model (Skipping-Based Recommender) which uses a technique called skipping, and is able to take into account these features. In a series of experimental studies, we put forward the various contributions that this model possesses and show that its quality surpasses that of the state-of-the-art.</EA>
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