Abstract: With the development of mobile Internet technology and the popularity of mobile terminals, there have been many social websites and applications on the Internet. As a social application, microblog has attracted a large number of users, with its convenience of operation and rapid propagation. A user receiving hundreds of microblogs every day, which leads to the situation of information overload, increases the difficulty of the user’s information and knowledge acquisition. On the other hand, more and more merchants treat microblog as a marketing platform, which makes the advertisements directed delivery become a problem with highly commercial value. Microblog user interest recognition can contribute to solve the problems discussed above. This paper proposes a topic augmented convolutional neural network approach to recognize user interest. By integrating the continuous semantic information and the discrete topic information, the proposed approach first obtains the category distribution of users’ microblogs. It then recognizes users’ interest through the maximum likelihood estimation over the category distribution of users’ microblogs. Experimental results show that the proposed topic augmented convolutional neural network approach outperforms the labeled LDA based approach and the traditional convolutional neural network approach significantly on the microblog classification and user interest recognition.

Key words: topic model, convolutional neural network (CNN), microblog classification, user interest recognition, microblog