Modeling User’s Interests Based on Image Semantics
Zeng Jin1,3, Lu Wei1,2(), Ding Heng1, Chen Haihua1
1School of Information Management, Wuhan University, Wuhan 430072, China 2Institute for Information Retrieval and Knowledge Mining, Wuhan University, Wuhan 430072, China 3School of Culture Management, Wuhan College of Media and Communications, Wuhan 430072, China
[Objective] This paper aims to predict the user’s interests accurately with a new modeling method based on the semantics of images shared on the microblogs. [Methods] First, we crawled the image data of Sina microblogging users. Then, we used high-level semantic information from these images. Finally, we predicted user’s interests based on the image semantic classifier by the SVM training. [Results] The proposed method could predict user’s interests effectively. Among the 169 Sina microblogging users, the precision, recall and F-values were 97.38%, 98.92% and 98.14%, respectively. [Limitations] The size of the test corpus needs to be expanded to have more comprehensive results. [Conclusions] The proposed model could predict user’s interests effectively, which lays some theoretical and technical foundations for the application of high-level image semantics.
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