User Similarity Measuring Algorithm Pasedon Tree-based Network

With the fast development of Internet technology, people can get more information and services from the Internet. It makes easier for people to live a good life in such a information age. But it also brings difficulties to us to choose. As the scale of e-commerce is expanding and the fast-growing number and variety of goods, customers need to spend a lot of time to find the products that they want to buy. In order to solve those problems, recommendation system came into being. In order to solve these problems,personalized recommendation system came into being. In the area of electronic commerce, a good recommender system not only provides users with a personalized service, but also establish close relationship with customers.People are becoming rely on recommendation system.This paper firstly introduced the Significance of the research on recommendation systems and similarity measures of tree-based network. Firstly we classified the similarity measures of tree into six categories, including operating-strategy-based,decomposition-strategy-based, path-comparison-based and node-comparison-based methods,bilateral matching method and largest public subtree method. The traditional matching algorithms have the problems such as cross-layer matching, not considering the hierarchical weights and so on. Subsequently, user interests tree-base network is built based on the user behavior and weighted path Decomposition Tree similarity matching algorithm is proposed based on the traditional path-match tree-based network similarity.At last, we apply this tree matching algorithm to collaborative filtering recommendation system by modifying the ser similarity comparison method.At the experimental stage, the experiments showed the proposed algorithm is superior to traditional methods. The proposed algorithm achieved the desired effect.