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Abstract

The ongoing explosion of web information calls for more intelligent and personalied methods towards better search result quality for advanced queries. Query log 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. This paper presents a new method that incorporates the notion of query nodes into PageRank model and integrates the implicite 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. The enhanced PageRank scores, coined QRank scores, can be computed offline; at query-time they are combined with query-specific relevance measures with virtually no overhead. Our preliminary experiments, based on real-life query-log and click-stream traces from eight different trial users indicate significant improvements in the precision of search results.