Predicting viewer’s watching behavior and live streaming content change for anchor recommendation

摘要

Recently, live streaming services attract millions of users’ participation and billions of capital investment. In each prevailing live streaming platform, there are thousands of anchors who are broadcasting concurrently, which means it is necessary for the platform to make recommendation to improve user experience. In such platforms, viewers change their watching preference dynamically, and anchors adjust their live content meanwhile. While there are many studies about predicting user’s (i.e., viewer’s) preference in literature, few methods proposed in literature can be used to predict live content’s change. As the recommendation target is online anchor’s live streaming that will be broadcasted in the next moment, we believe the prediction of the live streaming content is necessary for accurate recommendation. Therefore, in this paper, we study how to combine the prediction of viewer’s watching behavior and live content change for recommendation. We define a multi-task learning problem and propose a deep learning-based recommendation model, where we design two novel attention modules to capture viewer’s watching preference, anchor’s broadcasting preference, and loyal viewer’s preference related to each anchor. Experiments conducted on real datasets demonstrate the effectiveness of our proposed model.