Feature Weight Calculation Between Query and Texts Based on User Evaluation

This paper proposes a feature evaluation algorithm by using users click in order to analysis similarity between query and texts in search engines.This method gets features from potential evaluation of user search results because user’s clicks to search results reflect the inner relation between query and documents in search results.EM algorithm is used to calculate feature weights.It is difficult to know whether the model’s function is convergent because of its complexity.So the simulation annealing algorithm validates the model’s convergence as the complement of EM algorithm.The experiment is carried out in Baidu’s advertisement ranking.The samples have 100 advertisement and 144 132 queries related to these advertisement.The experiment shows its precision is 93.32% and its recall is 87.43%.All features in the experiment are convergent.