Inferring Social Network User’s Interest Based on Convolutional Neural Network

Learning microblog users’ interest has important significance for constructing more precise user profile, and can be useful for some commercial applications such as personalized advertisement, or potential customer analysis. Existing works generally utilize text mining or label propagation methods to solve this problem, which leverage either the user’s publicly available comments or the user’s social links, but not both. As we will show, these learning methods achieve limited precision rates. To address this challenge, we consider the interest inference task as a multi-value classification problem, and solve it using a convolutional neural network architecture. We innovatively present an ego social-attribute network model which integrates the target users’ attributes, social links and their comments, and represent the ego SA network as the input fed to CNN. As a result, we assign each microblog user one or more interest labels (such as “loving sports”), which is different from previous approaches using non-uniform interest keywords (such as “basketball”, “tennis”, etc.). Experimental results on SMP CUP and Zhihu dataset showed that the precision rate of user interest inference reached 77.9% at best.