Optimizing of Local Search Ranking Algorithm Based on User Behaviors Analysis
Jiang Zong-l · Computer Technology and Development · 2014
With the development of the local search,the rank results of general ranking algorithm cannot fully meet the needs of users. The characteristics of local search make the possibility that the user's search characteristics can be used more properly. By analyzing the user behaviors,the user behavior characteristic values are got. Then the SVM( Support Vector Machine) is employed to merge the user's behavior characteristic values into the local search algorithm. And the ranking algorithm is optimized. The average accuracy rate of the local search rank results and the top ten documents correlation have improved to some extent,after integrated into the user behavior features. The experimental results show that user behavior features allow ranking results can more easily and accurately response the user's interesting,and improve the user's search experience.