Desktop Search - How Contextual Information Influences Search Results & Rankings

1. MOTIVATION Sophisticated web search technology usually allows us to find appropriate documents in a few seconds. Finding these documents on our desktop is surprisingly more difficult, at least if we have been storing documents for a few years or more. This is improving somewhat with the recent crop of desktop search engines, but even with these tools, searching through our (relatively small set of) personal documents with the recent beta of Google Desktop Search is inferior to searching the (rather vast set of) documents on the web with Google. The main reason for this is that one of the distinguishing features of Google sophisticated ranking using PageRank and other features is unavailable on our desktop. This position paper explores first how the contextual information inferred from the users’ actions is used to extend search beyond simple full-text search. Second, we discuss how algorithms exploiting this information can provide efficient ranking of resources on our desktop. Regarding the first aspect, we propose activity-based metadata and relationships as sources of additional information in desktop search. This information, in many cases representing contextual information, is very useful for re-finding resources we already worked with, improving the recall in desktop search. For the second aspect, we discuss how PageRank-based ranking algorithms can exploit this contextual information and show how local and global ranking measures can be integrated in such a model, achieving at the same time personalization of rankings. Using contextual information thus shows considerable promise for extending efficient information access to our desktop, extending globally available information with user-centered activity-based information, and exploiting the unique information background we have available on our desktop. We are currently implementing first prototypes in the context of the open source Beagle project which aims to provide sophisticated desktop search in Linux.