Video eCommerce++: Toward Large Scale Online Video Advertising
The prevalence of online videos provides an opportunity for e-commerce companies to recommend their products in videos. In this paper, we propose an online video advertising system named Video eCommerce ++, to exhibit appropriate product ads to particular users at proper time stamps of videos, which takes into account video semantics, user shopping preference, and viewing behavior feedback. First, an incremental co-relation regression (ICRR) model is novelly proposed to construct the semantic association between videos and products. To meet the requirement of online advertising, ICRR is implemented in an incremental way to reduce the time complexity. User preference diffusion (UPD) is induced under the framework of heterogeneous information network to construct user-product association from two different e-commerce platforms, Tmall and MagicBox, which alleviates the problems of data sparsity and cold start. A video scene importance model (VSIM) is proposed to model the scene importance by utilizing the user viewing behavior, so that ads can be embedded at the most attractive positions in the video stream. To combine the outputs of ICRR, UPD, and VSIM, a unified distributed heterogeneous relation matrix factorization (D-HRMF) is applied for online video advertising, which is efficiently conducted in parallel to address the real-time update problem, so that the whole system can be performed in real time. Extensive experiments conducted on a variety of online videos from Tmall MagicBox demonstrate that Video eCommerce++ significantly outperforms the state-of-the-art advertising methods, and can handle large-scale data in real time.