A New Ranking Method for Personalized Search Engine
Junzhong Zhao · Journal of Taiyuan University of Science and Technology · 2013
A new ranking method is proposed based on the research on ranking algorithm for personalized search engines. SVD and k-means clustering algorithm are used for several times into different granularities to create two weighted interest trees: a document class tree and a word class tree. Each node in the tree is weighted and the weight represents the degree of interest of the user for that type of document or word. Then Bayesian classification algorithm and a scoring algorithm are applied to calculate the score of pages received by search engine. Finally,the scored pages are ranked in descending order. Experiments show that a more accurate rank can be achieved.