The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Rank-biased precision for measurement of retrieval effectiveness
Abstract
A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings. For example, recall is not well founded as a measure of satisfaction, since the user of an actual system cannot judge recall. Average precision is derived from recall, and suffers from the same problem. In addition, average precision lacks key stability properties that are needed for robust experiments. In this article, we introduce a new effectiveness metric, rank-biased precision , that avoids these problems. Rank-biased pre-cision is derived from a simple model of user behavior, is robust if answer rankings are extended to greater depths, and allows accurate quantification of experimental uncertainty, even when only partial relevance judgments are available.
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Cites
Cumulated gain-based evaluation of IR techniques
Järvelin, K., & Kekäläinen, J. (2002) · ACM Transactions on Information Systems · doi:10.1145/582415.582418 · Cited by 4,841
Accurately interpreting clickthrough data as implicit feedback
Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005) · Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1076034.1076063 · Cited by 1,408
How reliable are the results of large-scale information retrieval experiments?
Zobel, J. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.291014 · Cited by 579
Self-citation
Cited by
Test Collection Based Evaluation of Information Retrieval Systems
Sanderson, M. (2010) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000009 · Cited by 485
Chuklin, A., Markov, I., & de Rijke, M. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02294-4 · Cited by 398
A General Framework for Counterfactual Learning-to-Rank
Agarwal, A., Takatsu, K., Zaitsev, I., & Joachims, T. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331202 · Cited by 115
One-Shot Labeling for Automatic Relevance Estimation
MacAvaney, S., & Soldaini, L. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3592032 · Cited by 65
User Simulation for Evaluating Information Access Systems
Balog, K., & Zhai, C. (2024) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000098 · Cited by 52
Metadata
- Authors
- Moffat, A., & Zobel, J.
- Venue
- ACM Transactions on Information Systems
- Published
- 2008-12
- Volume
- 27
- Issue
- 1
- Pages
- 1-27
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W1968927634
- Cited by
- 620
- References
- 3
- FWCI
- 82.89
- Type
- journal article
- Language
- en
- Open access
- closed
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