Title :

A Web User Profiling Approach

Author(s) :

Hafri, Younes [Auteur]
FOX MIIRE [LIFL]
Institut National de l'Audiovisuel [INA]
Djeraba, Chaabane [Auteur]
Institut de Recherche sur les Composants logiciels et matériels pour l'Information et la Communication Avancée - UAR 3380 [IRCICA]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Stanchev, Peter [Auteur]
Bachimont, Bruno [Auteur]
Université de Technologie de Compiègne [UTC]
Institut National de l'Audiovisuel [INA]

Conference title :

Web Technologies and Applications, 5th Asian-Pacific Web Conference, APWeb 2003

City :

Xian

Country :

Chine

Start date of the conference :

2002-04-23

Publisher :

Springer, Berlin, Heidelberg

Publication date :

2003

English keyword(s) :

Markov Model
Hide Markov Model
web
Gravity Center

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Base de données [cs.DB]
Informatique [cs]/Multimédia [cs.MM]
Informatique [cs]/Recherche d'information [cs.IR]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

People display regularities in almost everything they do. This paper proposes characteristics of an idealized algorithm that would allow an automatic extraction of web user profil based on user navigation paths. We describe ...
Show more >People display regularities in almost everything they do. This paper proposes characteristics of an idealized algorithm that would allow an automatic extraction of web user profil based on user navigation paths. We describe a simple predictive approach with these characteristics and show its predictive accuracy on a large dataset from KDD-Cup web logs (a commercial web site), while using fewer computational and memory resources. To achieve this objective, our approach is articulated around three notions: (1) Applying probabilistic exploration using Markov models. (2) Avoiding the problem of Markov model high-dimensionality and sparsity by clustering web documents, based on their content, before applying the Markov analysis. (3) Clustering Markov models, and extraction of their gravity centers. On the basis of these three notions, the approach makes possible the prediction of future states to be visited in k steps and navigation sessions monitoring, based on both content and traversed paths.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL