The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Information retrieval system evaluation: effort, sensitivity, and reliability
Abstract
The effectiveness of information retrieval systems is measured by comparing performance on a common set of queries and documents. Significance tests are often used to evaluate the reliability of such comparisons. Previous work has examined such tests, but produced results with limited application. Other work established an alternative benchmark for significance, but the resulting test was too stringent. In this paper, we revisit the question of how such tests should be used. We find that the t-test is highly reliable (more so than the sign or Wilcoxon test), and is far more reliable than simply showing a large percentage difference in effectiveness measures between IR systems. Our results show that past empirical work on significance tests over-estimated the error of such tests. We also re-consider comparisons between the reliability of precision at rank 10 and mean average precision, arguing that past comparisons did not consider the assessor effort required to compute such measures. This investigation shows that assessor effort would be better spent building test collections with more topics, each assessed in less detail.
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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 · cited 5×
“This we took to be evidence that the dependency between large topic sets has a substantial influence on the error rate measured by the methodology used by Zobel (1998), by Voorhees and Buckley (2002), and used so far in this paper.”
BackgroundMethodInfluentialSelf-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
Self-citationInteractive IR User Study Design, Evaluation, and Reporting
Liu, J., & Shah, C. (2019) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02319-4 · Cited by 26
The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models
Pradeep, R., Thakur, N., Upadhyay, S., Campos, D., Craswell, N., Soboroff, I., Dang, H. T., & Lin, J. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730090 · Cited by 14
“Here, we adopt the meta-evaluation methodology common in the information retrieval literature dating back several decades [4, 5, 14, 37, 44, 52].”
A Large-Scale Study of Relevance Assessments with Large Language Models Using UMBRELA
Upadhyay, S., Pradeep, R., Thakur, N., Campos, D., Craswell, N., Soboroff, I., & Lin, J. (2025) · Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR) · doi:10.1145/3731120.3744605 · Cited by 11
WANDS: Dataset for Product Search Relevance Assessment
Chen, Y., Liu, S., Liu, Z., Sun, W., Baltrunas, L., & Schroeder, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-99736-6_9 · Cited by 10
Metadata
- Authors
- Sanderson, M., & Zobel, J.
- Venue
- Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2005-08-15
- Pages
- 162-169
- OpenAlex
- W2015338694
- Cited by
- 369
- References
- 1
- FWCI
- 29.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sanderson, M., & Zobel, J. (2005). Information retrieval system evaluation: effort, sensitivity, and reliability. Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1076034.1076064
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