document: Mining search engine query logs for query recommendation This paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial strength search engine . In order to get a more comprehensive solution , we combine two methods together . On the one hand , we study and model search engine users ' sequential search behavior , and interpret this consecutive search behavior as client-side query refinement , that should form the basis for the search engine 's own query refinement process . On the other hand , we combine this method with a traditional content based similarity method to compensate for the high sparsity of real query log data , and more specifically , the shortness of most query sessions . To evaluate our method , we use one hundred day worth query logs from SINA ' search engine to do off-line mining . Then we analyze three independent editors evaluations on a query test set . Based on their judgement , our method was found to be effective for finding related queries , despite its simplicity . In addition to the subjective editors ' rating , we also perform tests based on actual anonymous user search sessions .
doc_bio_tags: B O O B I O O B O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O