Abstract：Due to the ever-increasing amount of digital contents in the internet, the traditional information retrieval technology is unable to meet the demands for high precision information of different users. In this paper, a personalized query expansion method based on multiple semantic relationships is proposed. It is used for personalized search based on social tagging systems. An tag-topic model is utilized to generate the user interesting model. Therefore, more precise semantics can be captured. The performance of the search can also be improved. Based on the user model, a personalized search method based on multiple semantic relationships from social data is further presented to select suitable expansion terms. Experiments conducted on a large social tagging dataset show that the proposed method outperforms several non-personalized methods as well as the existing personalized search methods based on social tagging systems.
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2017, Vol. 30 
