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Abstract
In this paper, we present a study on video viewing behavior. Based on a well-suited Markovian model, we have developed a clustering algorithm called K-models and inspired by the K-means technique to cluster and analyze behaviors. These models are constructed using the different actions proposed to the user while he is viewing a video sequence (play, pause, forward, rewind, jump, stop). We have applied our algorithm with a movie trailer mining tool. This tool allows users to perform searches on basic attributes (cast, director, onscreen date…) and to watch selected trailers. With an appropriate server, we log every action to analyze behaviors. First results obtained from a set of beta users reveal interesting typical behaviors.