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Combining Interaction and Content for Feedback-Based Ranking

  • Emanuele Di Buccio
  • Massimo Melucci
  • Dawei Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6653)

Abstract

The paper is concerned with the design and the evaluation of the combination of user interaction and informative content features for implicit and pseudo feedback-based document re-ranking. The features are observed during the visit of the top-ranked documents returned in response to a query. Experiments on a TREC Web test collection have been carried out and the experimental results are illustrated. We report that the effectiveness of the combination of user interaction for implicit feedback depends on whether document re-ranking is on a single-user or a user-group basis. Moreover, the adoption of document re-ranking on a user-group basis can improve pseudo-relevance feedback by providing more effective document for expanding queries.

Keywords

Relevant Document User Study User Behavior Query Expansion Test Collection 
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 2011

Authors and Affiliations

  • Emanuele Di Buccio
    • 1
  • Massimo Melucci
    • 1
  • Dawei Song
    • 2
  1. 1.University of PaduaItaly
  2. 2.The Robert Gordon UniversityUK

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