LiveSense: Contextual Advertising in Live Streaming Videos

Live streaming has become a new form of entertainment, which attracts hundreds of millions of users worldwide. The huge amount of multimedia data in live streaming platforms creates tremendous opportunities for online advertising. However, existing state-of-the-art video advertising strategies (e.g., pre-roll and contextual mid-roll advertising) that rely on analyzing the whole video, are not applicable to live streaming videos. This paper describes a novel monetization framework, named LiveSense, for live streaming videos, which is able to display a contextually relevant ad at a suitable timestamp in a non-intrusive way. Specifically, given a live streaming video, we first employ a deep neural network to determine whether the current moment is appropriate for displaying an ad using the historical streaming data. Then, we detect a set of candidate ad insertion areas by incorporating image saliency, background map, and location priorities, so that the ad is displayed over the non-important area. We introduce three types of relevance metrics including textual relevance, global visual relevance and local visual relevance to select the contextually relevant ad. To minimize user intrusiveness, we initially display the ad at a non-important area. If the user is interested in the ad, we will show the ad in an overlaid window with a translucent background. Empirical evaluation on a real-world dataset demonstrates that our proposed framework is able to effectively display ads in live streaming videos while maintaining users' online experience.