Social-group-based ranking algorithms for cold-start video recommendation

Video recommendation has become an essential part of online video services. Cold start, a problem relatively common in the practical online video recommendation service, occurs when the user who needs video recommendation has no viewing history. (Cold start consists of the new-user problem and the new-item problem. In this paper, we discuss the new-user one.) A promising approach to resolve this problem is to capitalize on information in online social networks (OSNs): Videos viewed by a user’s friend may be good candidates for recommendation. However, in practice, this information is also quite limited, either because of insufficient friends or lack of abundant viewing history of friends. In this work, we utilize social groups with richer information to recommend videos. It is common that users may be affiliated with multiple groups in OSNs. Through members within the same group, we can reach a considerably larger set of users and hence more candidate videos for recommendation. In this paper, by collaborating with Tencent Video, we propose a social-group-based algorithm to produce personalized video recommendations by ranking candidate videos from the groups a user is affiliated with. This algorithm was implemented and tested in the Tencent Video service system. Compared with two state-of-the-art methods, the proposed algorithm not only improves the click-through rate, but also recommends more diverse videos.