Learning Video Preferences Using Visual Features And Closed Captions

Viewers of video now have more choices than ever. As the number of choices increases, the task of searching through these choices to locate video of interest is becoming more difficult. Current methods for learning a viewer's preferences in order to automate the search process rely either on video having content descriptions or on having been rated by other viewers identified as being similar. However, much video exists that does not meet these requirements. To address this need, we use hidden Markov models to learn the preferences of a viewer by combining visual features and closed captions. We validate our approach by testing the learned models on a data set composed of features drawn from movies and user ratings obtained from publicly available data sets.