A Hybrid Relevance-Feedback Approach to Text Retrieval

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2633)


Relevance feedback (RF) has been an effective query modification approach to improving the performance of information retrieval (IR) by interactively asking a user whether a set of documents are relevant or not to a given query concept. The conventional RF algorithms either converge slowly or cost a user’s additional efforts in reading irrelevant documents. This paper surveys several RF algorithms and introduces a novel hybrid RF approach using a support vector machine (HRFSVM), which actively selects the uncertain documents as well as the most relevant ones on which to ask users for feedback. It can efficiently rank documents in a natural way for user browsing. We conduct experiments on Reuters-21578 dataset and track the precision as a function of feedback iterations. Experimental results have shown that HRFSVM significantly outperforms two other RF algorithms.


Support Vector Machine Relevant Document Support Vector Machine Model Relevance Feedback Document Retrieval 
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 2003

Authors and Affiliations

  1. 1.Tsinghua UniversityBeijingP.R. China
  2. 2.University of Arkansas at Little RockLittle RockUSA
  3. 3.University of MunichMunichGermany
  4. 4.Siemens AG, Corporate TechnologyMunichGermany

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