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 qu...

• Type: Conference
• Year: 2006
• Where: WWW
• Authors: Zhiyong Zhang, Olfa Nasraoui
• Keywords: Client-side Query Refinement, Internet Technology, Query Refinement Process, Search Engine Query, WWW 2006