Low-Rank Multi-View Embedding Learning for Micro-Video Popularity Prediction
Recently, a prevailing trend of user generated content (UGC) on social media sites is the emerging micro-videos. Micro-videos afford many potential opportunities ranging from network content caching to online advertising, yet there are still little efforts dedicated to research on micro-video understanding. In this paper, we focus on popularity prediction of micro-videos by presenting a novel low-rank multi-view embedding learning framework. We name it as transductive low-rank multi-view regression (TLRMVR), and it is capable of boosting the performance of micro-video popularity prediction by jointly considering the intrinsic representations of the source and target samples. In particular, TLRMVR integrates low-rank multi-view embedding and regression analysis into a unified framework such that the lowest-rank representation shared by all views not only captures the global structure of all views, but also indicates the regression requirements. The framework is formulated as a regression model and it seeks a set of view-specific projection matrices with low-rank constraints to map multi-view features into a common subspace. In addition, a multi-graph regularization term is constructed to improve the generalization capability and further prevents the overfitting problem. Extensive experiments conducted on a publicly available dataset demonstrate that our proposed method achieve promising results as compared with state-of-the-art baselines.