Summarizing local context to personalize global web search

The PC Desktop is a very rich repository of personal information, efficiently capturing user's interests. In this paper we propose a new approach towards an automatic personalization of web search in which the user specific information is extracted from such local desktops, thus allowing for an increased quality of user profiling, while sharing less private information with the search engine. More specifically, we investigate the opportunities to select personalized query expansion terms for web search using three different desktop oriented approaches: summarizing the entire desktop data, summarizing only the desktop documents relevant to each user query, and applying natural language processing techniques to extract dispersive lexical compounds from relevant desktop resources. Our experiments with the Google API showed at least the latter two techniques to produce a very strong improvement over current web search.

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