The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
User performance versus precision measures for simple search tasks
Abstract
Several recent studies have demonstrated that the type of improvements in information retrieval system effectiveness reported in forums such as SIGIR and TREC do not translate into a benefit for users. Two of the studies used an instance recall task, and a third used a question answering task, so perhaps it is unsurprising that the precision based measures of IR system effectiveness on one-shot query evaluation do not correlate with user performance on these tasks. In this study, we evaluate two different information retrieval tasks on TREC Web-track data: a precision-based user task, measured by the length of time that users need to find a single document that is relevant to a TREC topic; and, a simple recall-based task, represented by the total number of relevant documents that users can identify within five minutes. Users employ search engines with controlled mean average precision (MAP) of between 55% and 95%. Our results show that there is no significant relationship between system effectiveness measured by MAP and the precision-based task. A significant, but weak relationship is present for the precision at one document returned metric. A weak relationship is present between MAP and the simple recall-based task.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Broder, A. (2002) · ACM SIGIR Forum · doi:10.1145/792550.792552 · Cited by 1,944
Lecture Notes in Computer Science 1867
Baldwin, J., Martin, T., & Tzanavari, A. (2000) · Cited by 1,643
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
Readings in information retrieval
Jones, K. S., & Willett, P. (1997) · Morgan Kaufmann Publishers Inc. eBooks · Cited by 1,069
TREC: Experiment and evaluation in information retrieval
Vicedo, J. L., & Gómez, J. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20583 · Cited by 837
Retrieval evaluation with incomplete information
Buckley, C., & Voorhees, E. M. (2004) · Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1008992.1009000 · Cited by 736
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×
“While some relevant documents may thus remain unidentified, this approach has been demonstrated to work effectively for the comparison of different retrieval systems [20].”
BackgroundEvaluating Evaluation Measure Stability
Buckley, C., & Voorhees, E. M. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130373 · Cited by 559
The CRANFIELD TESTS ON INDEX LANGUAGE DEVICES
CLEVERDON, C. (1967) · Aslib Proceedings · doi:10.1108/eb050097 · Cited by 509
Variations in relevance judgments and the measurement of retrieval effectiveness
Voorhees, E. M. (2000) · Information Processing & Management · doi:10.1016/s0306-4573(00)00010-8 · Cited by 504
Cited by
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
Zheng, Z., Yang, Y., Niu, X., Dai, H.-N., & Zhou, Y. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2785963 · Cited by 788
Learning diverse rankings with multi-armed bandits
Radlinski, F., Kleinberg, R., & Joachims, T. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390255 · Cited by 503
Test Collection Based Evaluation of Information Retrieval Systems
Sanderson, M. (2010) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000009 · Cited by 485
A 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
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
- Turpin, A., & Scholer, F.
- Venue
- Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2006-08-06
- Pages
- 11-18
- OpenAlex
- W2169783907
- Cited by
- 389
- References
- 23
- FWCI
- 15.13
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: User performance versus precision measures for simple search tasks. https://app.cochat.ai/papers/10.1145/1148170.1148176
- The paper
- Turpin, A., & Scholer, F. (2006). User performance versus precision measures for simple search tasks. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1148170.1148176
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
