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Demonstration of Improved Search Result Relevancy Using Real-Time Implicit Relevance Feedback

Surf Canyon has developed real-time implicit personalization technology for web search and implemented the technology in a browser extension that can dynamically modify search engine results pages (Google, Yahoo!, and Live Search). A combination of explicit (queries, reformulations) and implicit (clickthroughs, skips, page reads, etc.) user signals are used to construct a model of instantaneous user intent. This user intent model is combined with the initial search result rankings in order to present recommended search results to the user as well as to reorder subsequent search engine results pages after the initial page. This paper will use data from the first three months of Surf Canyon usage to show that a user intent model built from implicit user signals can dramatically improve the relevancy of search results.