Fully Automated Learning for Application-Specific Web Video Classification

Personalization applications such as content recommendations, product recommendations and advertisements, and social network related recommendations, can be quite beneficial for both service providers and users. Such applications need to understand user preferences in order to provide customized services. As user engagement with web videos has grown significantly, understanding user preferences based on videos viewed looks promising. The above requires ability to classify web videos into a set of categories appropriate for the personalization application. However, such categories may be substantially different from common categories like Sports, Music, Comedy, etc. used by video sharing websites, leading to lack of labeled training videos for such categories. In this paper, we study the feasibility and effectiveness of a fully automated framework to obtain training videos to enable classification of web videos to any arbitrary set of categories, as desired by the personalization application. We investigate the desired properties in training data that can lead to high performance of the trained classification models. We then develop an approach to identify and score keywords based on their suitability to retrieve training videos, with the desired properties, for the specified set of categories. Experimental results on several sets of categories demonstrate the ability of the proposed approach to obtain effective training data, and hence achieve high video classification performance.