Query-Log Based Authority Analysis for Web Information Search

Abstract. The ongoing explosion of web information calls for more intelligent and personalized methods towards better search result quality for advanced queries. Query logs and click streams obtained from web browsers or search engines can contribute to better quality by exploiting the collaborative recommendations that are implicitly embedded in this information. I will present a new method that incorporates the notion of query nodes into the PageRank model and integrates the implicit relevance feedback given by click streams into the automated process of authority analysis. This approach generalizes the well-known random-surfer model into a random-expert model that mimics the behavior of an expert user in an extended session consisting of queries, query refinements, and result-navigation steps. I will then present some preliminary experiments carried out by the authors based on real-life query-log and click-stream traces from eight different trial users. These experiments indicate significant improvements in the precision of search results. Finally, I will present some of the potential points of future work that can be carried out in this area of research.