A support vector method for optimizing average precision

@inproceedings{Yue2007ASV,
  title={A support vector method for optimizing average precision},
  author={Yisong Yue and Thomas Finley and Filip Radlinski and Thorsten Joachims},
  booktitle={SIGIR},
  year={2007}
}
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… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 505 CITATIONS

Bipartite Ranking: a Risk-Theoretic Perspective

VIEW 11 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

SEPIM: Secure and Efficient Private Image Matching

VIEW 6 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Joint Structural Learning to Rank with Deep Linear Feature Learning

  • IEEE Transactions on Knowledge and Data Engineering
  • 2015
VIEW 6 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Learning to Rank Academic Experts in the DBLP Dataset

VIEW 11 EXCERPTS
CITES METHODS
HIGHLY INFLUENCED

Structural Bregman Distance Functions Learning to Rank with Self-Reinforcement

  • 2014 IEEE International Conference on Data Mining
  • 2014
VIEW 12 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2007
2019

CITATION STATISTICS

  • 117 Highly Influenced Citations

  • Averaged 28 Citations per year from 2017 through 2019