Paper Title:
Improving Relevance Feedback via Using Support Vector Machines
  Abstract

Traditional relevance feedback technique could help improve retrieval performance. It usually utilize the most frequent terms in the relevant documents to enrich the user’s initial query. We re-examine this method and find that many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. This paper introduces a Support Vector Machines Based method to improve the retrieval results. Firstly, the classifier is trained on the feedback documents. Then, we can utilize this classifier to classify the rest of the documents and move relevant documents to the front of irrelevant documents. This new approach avoids modifying the initial query, so it’s a new direction for the relevance feedback techniques. Our Experiments on TREC dataset demonstrate that retrieval effectiveness can be improved more than 24.37% when our proposed approach is used.

  Info
Periodical
Advanced Materials Research (Volumes 255-260)
Edited by
Jingying Zhao
Pages
2028-2032
DOI
10.4028/www.scientific.net/AMR.255-260.2028
Citation
Z. L. Chen, Y. Lu, "Improving Relevance Feedback via Using Support Vector Machines", Advanced Materials Research, Vols. 255-260, pp. 2028-2032, 2011
Online since
May 2011
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Price
$32.00
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