Discovering Similar User Models Based on Interest Tree

With the explosive development of Internet and Social Networking Services (SNS), more and more people begin to get information from others. So how to find users, which have similar interests, is becoming an important issue. The traditional method is using a vector to calculate the similarities between user models. The similarities between user models are measured by one value. This method is very simple, but some useful details are lost. Users do not know where they are similar in detail. Particularly, the existing approaches cannot calculate the similarity between users under the different interest trees of user models. Aiming at solving these problems, a method which expresses and calculates the similarity between two user models in different granularities is proposed in this paper. Node Structure Similarity (NSS), Interest Theme Similarity (ITS), Comprehensive Interest Similarity (CIS) and Dynamical Comprehensive Interest Similarity (DCIS) are considered to describe the similarities between user models. NSS reflects to structural similarity of interest tree. ITS is the interest theme similarity between user´s interest trees. CIS is a comprehensive similarity which has combined NSS with ITS. DCIS is not only calculated by NSS and ITS but also considered the weight of NSS and ITS. Experimental results show that DCIS is the most reasonable one among the three methods mentioned above.