The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A support vector method for optimizing average precision
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.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
THE PROBABILITY RANKING PRINCIPLE IN IR
ROBERTSON, S. E. (1977) · Journal of Documentation · doi:10.1108/eb026647 · Cited by 1,103
Cited by
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273513 · Cited by 1,960
Learning diverse rankings with multi-armed bandits
Radlinski, F., Kleinberg, R., & Joachims, T. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390255 · Cited by 503
Self-citationPerson Re-identification by Salience Matching
Zhao, R., Ouyang, W., & Wang, X. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.314 · Cited by 466
Learning With Average Precision: Training Image Retrieval With a Listwise Loss
Revaud, J., Almazan, J., Rezende, R., & Souza, C. D. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00521 · Cited by 408
End-to-End Training of Object Class Detectors for Mean Average Precision
Henderson, P., & Ferrari, V. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54193-8_13 · Cited by 373
Metadata
- Authors
- Yue, Y., Finley, T., Radlinski, F., & Joachims, T.
- Venue
- Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2007-07-23
- Pages
- 271-278
- OpenAlex
- W2127176025
- Cited by
- 704
- References
- 3
- FWCI
- 32.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007). A support vector method for optimizing average precision. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1277741.1277790
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