Personalizing Search Using Socially Enhanced Interest Model Built from the Stream of User's Activity

Older studies have proved that when searching information on the Web, users tend to write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are very ambiguous, search engines tend to find the most popular meaning - someone who does not know anything about cascading stylesheets might search for a music band called css and be very surprised about the results. In this paper we propose a method which can infer additional keywords for a search query by leveraging a social network context and a method to build this network from the stream of user's activity on the Web. The approach was evaluated on real users using a personalized proxy server platform. The query expansion method was integrated into Google search engine and where possible, the original query was expanded and additional search results were retrieved and displayed. 70% of the expanded results were clicked and we observed a significant increase of time that the users spent on the expanded results when compared to the time spent on standard results.

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