The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Listwise approach to learning to rank: theory and algorithm
Abstract
This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimizing a loss function defined on the predicted list and the ground-truth list. Existing work on the approach mainly focused on the development of new algorithms; methods such as RankCosine and ListNet have been proposed and good performances by them have been observed. Unfortunately, the underlying theory was not sufficiently studied so far. To amend the problem, this paper proposes conducting theoretical analysis of learning to rank algorithms through investigations on the properties of the loss functions, including consistency, soundness, continuity, differentiability, convexity, and efficiency. A sufficient condition on consistency for ranking is given, which seems to be the first such result obtained in related research. The paper then conducts analysis on three loss functions: likelihood loss, cosine loss, and cross entropy loss. The latter two were used in RankCosine and ListNet. The use of the likelihood loss leads to the development of a new listwise method called ListMLE, whose loss function offers better properties, and also leads to better experimental results.
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Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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
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
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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
Self-citationA Theoretical Analysis of NDCG Type Ranking Measures
Wang, Y., Wang, L., Li, Y., He, D., Liu, T.-Y., & Chen, W. (2013) · arXiv · doi:10.48550/arxiv.1304.6480 · Cited by 351
Self-citationThe LambdaLoss Framework for Ranking Metric Optimization
Wang, X., Li, C., Golbandi, N., Bendersky, M., & Najork, M. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271784 · Cited by 136
Adaptive Information Seeking for Open-Domain Question Answering
Zhu, Y., Pang, L., Lan, Y., Shen, H., & Cheng, X. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.293 · Cited by 24
Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3
“This reward model guides reinforcement learning to optimize the language model’s policy for generating high-reward outputs, with a KL divergence constraint ensuring alignment with the original supervised fine-tuned model or the reference policy 𝜋 ref .”
Metadata
- Authors
- Xia, F., Liu, T.-Y., Wang, J., Zhang, W., & Li, H.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 1192-1199
- OpenAlex
- W2091158010
- Cited by
- 702
- References
- 4
- FWCI
- 23.87
- Type
- conference paper
- Language
- en
- Open access
- closed
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