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Extracting interesting association rules from toolbar data

Toolbar navigation logs provide rich data for enhancing information discovery on the Web. The value of this data resides in its scope, which goes beyond that of traditional query-mining data sources, such as search-engine logs. In this paper we present a methodology for extracting relevant association rules for queries, based on historic user navigational data. In addition, we propose a graph-based approach for extracting related queries and URLs for a given query.