The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Learning to rank for information retrieval
Abstract
This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.
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Cites
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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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Johnson, J. M., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0192-5 · Cited by 3,032
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
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-citationHeterogeneous Graph Transformer
Hu, Z., Dong, Y., Wang, K., & Sun, Y. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380027 · Cited by 1,646
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
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Metadata
- Authors
- Liu, T.-Y.
- Venue
- Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2010-07-19
- Pages
- 904-904
- OpenAlex
- W1973435495
- Cited by
- 1,856
- References
- 7
- FWCI
- 88.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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