The Journal of Machine Learning Research
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Ranking documents in response to users' information needs is a challenging task, due, in part, to the dynamic nature of users' interests with respect to a query. I hypothesize that the interests of a given user are similar to the interests of the broader community of which he is a part and propose an innovative method that uses social media to characterize the interests of the community and use this characterization to improve future rankings. By generating a community interest vector (CIV) for a given query, we use community interest to alter the ranking score of individual documents retrieved by the query. The CIV is based on a continuously updated set of recent (daily or past few hours) user-oriented text data. The user-oriented data can be user blogs or user comment tagged news. Preliminary evaluation shows that the new ranking method significantly improves ranking performance.