Hybrid Query Session and Content-based Recommendations for Enhanced Search
Zhiyong Zhang, Olfa Nasraoui · 2006
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 comprehensive solution, we combine two methods together. First, 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. This query refinement process is exploited to learn useful relations and build fuzzy associative memories that help generate related queries via a fuzzy inference process. Second, 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.