关键词: 用户兴趣描述文件, PLSA, 潜语义空间, Zipf分布, 用户兴趣层次树

Abstract: To mine user's latent interests and make relevantly the clustering analysis during personalized search, it is proptxsed to combine the characteristics of Zipf distribution in latent semantic space with PLSA (the probability latent semantic analysis), so as to gain the semantemes of the whole text. Namely, the principle of Zipf distribution is introduced to find out the latent semantic space of files, where the user interest is clustered according to underlying factors and a user interest hierarchy tree is built in user profile. Experimental results show that the clustering result as proposed is clearly superior to that by the conventional VSM (vector space model) algorithm. In addition, the results of the recommended personalized experiment based on well-known CTI data set also indicates fully that the description of user profile on the basis of latent semantic space coincides actually with the user interest.