Learning video browsing behavior and its application in the generation of video previews

Abstract

With more and more streaming media servers becoming commonplace, streaming video has now become a popular medium of instruction, advertisement, and entertainment. With such prevalence comes a new challenge to the servers: Can they track browsing behavior of users to determine what interest users? Learning this information is potentially valuable not only for improved customer tracking and context-sensitive e-commerce, but also in the generation of fast previews of videos for easy pre-downloads. In this paper, we present a formal learning mechanism to track video browsing behavior of users. This information is then used to generate fast video previews. Specifically, we model the states a user transitions while browsing through videos to be the hidden states of a Hidden Markov Model. We estimate the parameters of the HMM using maximum likelihood estimation for each sample observation sequence of user interaction with videos. Video previews are then formed from interesting segments of the video automatically inferred from an analysis of the browsing states of viewers. Audio coherence in the previews is maintained by selecting clips spanning complete clauses containing topically significant spoken phrases. The utility of learning video browsing behavior is demonstrated through user studies and experiments.