Incorporating message embedding into co-factor matrix factorization for retweeting prediction

With the rapid growth of Web 2.0, social media has become a prevalent information sharing and spreading platform, where users can retweet interesting messages. To better understand the propagation mechanism for information diffusion, it is necessary to model the user retweeting behavior and predict future retweets. Some existing work in retweeting prediction based on matrix factorization focuses on using user-message interaction information, user information and social influence information, etc. The challenge of improving prediction performance is how to jointly perform deep representation of these information to solve the sparsity problem and then learn a more comprehensive retweeting behavior model. Inspired by word2vec and co-factor matrix factorization model, this paper proposes a hybrid model, called HCFMF, for learning users' retweeting behavior, it first computes the message content similarity by considering the message co-occurrence, the author information and word2vec based low-dimensional representation of content, then, jointly decomposes the user-message matrix and message-message similarity matrix based on a co-factorization model. We empirically evaluate the performance of the proposed model on real world weibo datasets. Experimental results show that taking the dense representation of author and content information into consideration could allow us make more accurate analysis of users' retweeting patterns. The mined patterns could serve as a feedback channel for both consumers and management departments.