Personalized video recommendation based on cross-platform user modeling
Online propagation of videos has surged up to an unparalleled level. Most personalized video recommendation methods are based on single-platform user modeling, which suffer from data sparsity and cold-start issues. In this paper, we introduce cross-platform user modeling as a solution by smartly aggregating user information from different platforms. Unlike traditional recommendation methods where sufficient user information is assumed available in the target platform, this proposed method works well when there is little knowledge about users' interests in the target platform. While considering the difference of user behaviors in different platforms, on one hand, we enrich user profile in the target platform with related information in the auxiliary platform. On the other hand, we transfer the collaborative relationship defined in behaviors from the auxiliary platform to the target platform. Carefully designed experiments have demonstrated the effectiveness of the proposed method.