The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Evaluating Evaluation Measure Stability
Abstract
This paper presents a novel way of examining the accuracy of the evaluation measures commonly used in information retrieval experiments. It validates several of the rules-of-thumb experimenters use, such as the number of queries needed for a good experiment is at least 25 and 50 is better, while challenging other beliefs, such as the common evaluation measures are equally reliable. As an example, we show that Precision at 30 documents has about twice the average error rate as Average Precision has. These results can help information retrieval researchers design experiments that provide a desired level of confidence in their results. In particular, we suggest researchers using Web measures such as Precision at 10 documents will need to use many more than 50 queries or will have to require two methods to have a very large difference in evaluation scores before concluding that the two methods are actually different.
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Citations
Cites
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 1×
“The test collections created through the TREC workshops have been validated by demonstrating the stability of relative retrieval scores despite incomplete relevance judgments [8] [28] and different opinions as to what constitutes a relevant document [23].”
BackgroundVariations in relevance judgments and the measurement of retrieval effectiveness
Voorhees, E. M. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.291017 · Cited by 303
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
Using Language Models for Information Retrieval
Hiemstra, D. (2001) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.570441 · Cited by 448
The Philosophy of Information Retrieval Evaluation
Voorhees, E. M. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45691-0_34 · Cited by 389
Self-citationUser performance versus precision measures for simple search tasks
Turpin, A., & Scholer, F. (2006) · Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1148170.1148176 · Cited by 389
A framework for evaluating automatic indexing or classification in the context of retrieval
Golub, K., Soergel, D., Buchanan, G., Tudhope, D., Lykke, M., & Hiom, D. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23600 · Cited by 49
Metadata
- Authors
- Buckley, C., & Voorhees, E. M.
- Venue
- ACM SIGIR Forum
- Published
- 2017-08-02
- Volume
- 51
- Issue
- 2
- Pages
- 235-242
- ISSN
- 0163-5840 (ISSN-L)
- OpenAlex
- W2073722401
- Cited by
- 559
- References
- 2
- FWCI
- 36.68
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
