Improving Re-ranking of Search Results Using Collaborative Filtering

  • U Rohini
  • Vamshi Ambati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4182)


Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and the context of search. Re-ranking of these results to reflect the most relevant results to the user, using a user profile built from the relevance feedback has proved to provide good results. Our approach assumes implicit feedback gathered from a search engine query logs and learn a user profile. The user profile typically runs into sparsity problems due to the sheer volume of the WWW. Sparsity refers to the missing weights of certain words in the user profile. In this paper, we present an effective re-ranking strategy that compensates for the sparsity in a user’s profile, by applying collaborative filtering algorithms. Our evaluation results show an improvement in precision over approaches that use only a user’s profile.


Relevance Feedback Collaborative Filter Implicit Feedback Open Directory Project Query Expansion Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • U Rohini
    • 1
  • Vamshi Ambati
    • 2
  1. 1.Language Technologies Research CenterInternational Institute of Information TechnologyHyderabadIndia
  2. 2.Regional Mega Scanning CenterInternational Institute of Information TechnologyHyderabadIndia

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