VideoSense – Towards Effective Online Video Advertising

With Internet delivery of video content surging to an unprecedented level, online video advertising is becoming increasingly pervasive. In this paper, we present a novel advertising system for online video service called VideoSense, which automatically associates the most relevant video ads with online videos and seamlessly inserts the ads at the most appropriate positions within each individual video. Unlike most current video-oriented sites that only display a video ad at the beginning or the end of a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within the video stream. Given an online video, VideoSense is able to detect a set of candidate ad insertion points based on content discontinuity and attractiveness, select a list of relevant candidate ads ranked according to global textual relevance, and compute local visual-aural relevance between each pair of insertion points and ads. To support contextually relevant and less intrusive advertising, the ads are expected to be inserted at the positions with highest discontinuity and lowest attractiveness, while the overall global and local relevance is maximized. We formulate this task as a nonlinear 0-1 integer programming problem and embed these rules as constraints. The experiments have proved the efiectiveness of VideoSense for online video advertising.