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.%随着数字内容不断增长,信息检索技术已经不能满足不同用户对高精度信息内容获取的需求.文中提出基于多语义关系的个性化查询扩展方法,并应用于基于社会化标签的个性化搜索系统.模型使用标签-主题模型对用户兴趣模型进行建模,能够更有效地表达语义和提升搜索效果.在此基础上,进一步提出基于多语义关系的个性化查询扩展方法,利用社会化标签的多重语义特征进行扩展词的选择.在大规模真实社会化标签数据集上的实验表明,文中方法优于非个性化搜索及其它基于社会化标签系统的个性化查询扩展方法.
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