Model Detection for User Behavior in Video Sessions
نویسندگان
چکیده
Résumé. Cet article présente l’étude de l’extraction des comportements type des utilsateurs visionnant des vidéos, modélisés comme des matrices stochastiques de chaînes de Markov finies. Ces comportements sont regroupés à l’aide d’une mesure de dissimilarité basée sur la dissimilarité de Kullbach-Leibler entre les probabilités de distribution et le centre de chacun des groupes correspond au modèle ayant généré les comportements assignés au groupe. Ce choix s’explique par le lien que nous avons établi entre la dissimilarité entre un comportement et un modèle, et la probabilité que le modèle ait généré le comportement. Les résultats expérimentaux qui évaluent la qualité du regroupement valident notre choix des modèles.
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