Mining search engine query logs for social filtering-based query recommendation

This paper presents a simple and intuitive method for mining search engine query logs for fast social filtering, where searchers are provided with dynamic query recommendations on a large-scale industrial-strength search engine. We adopt a dynamic approach that is able to absorb new and recent trends in web usage trends on search engines, while forgetting outdated trends, thus adapting to dynamic changes in web user’s interests. In order to get well-rounded recommendations, we combine two methods: first, we 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. This query refinement process is exploited to learn useful information that helps generate related queries. Second, we combine this method with a traditional text or content based similarity method to compensate for the shortness of query sessions and sparsity of real query log data.