A support vector method for optimizing average precision

  • Authors:
  • Yisong Yue;Thomas Finley;Filip Radlinski;Thorsten Joachims

  • Affiliations:
  • Cornell University;Cornell University;Cornell University;Cornell University

  • Venue:
  • SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2007

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Abstract

Machine learning is commonly used to improve ranked retrieval systems. Due to computational difficulties, few learning techniques have been developed to directly optimize for mean average precision (MAP), despite its widespread use in evaluating such systems. Existing approaches optimizing MAP either do not find a globally optimal solution, or are computationally expensive. In contrast, we present a general SVM learning algorithm that efficiently finds a globally optimal solution to a straightforward relaxation of MAP. We evaluate our approach using the TREC 9 and TREC 10 Web Track corpora (WT10g), comparing against SVMs optimized for accuracy and ROCArea. In most cases we show our method to produce statistically significant improvements in MAP scores.