The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
A survey on the use of relevance feedback for information access systems
Open accessAbstract
Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems.
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
Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,505
Philosophical transactions of the Royal Society of London. B
Press, H. (1886) · Cited by 10,080
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096
A STATISTICAL INTERPRETATION OF TERM SPECIFICITY AND ITS APPLICATION IN RETRIEVAL
SPARCK JONES, K. (1972) · Journal of Documentation · doi:10.1108/eb026526 · Cited by 4,505
AN ESSAY TOWARDS SOLVING A PROBLEM IN THE DOCTRINE OF CHANCES
BAYES (1958) · Biometrika · doi:10.1093/biomet/45.3-4.296 · Cited by 2,911
Relevance feedback in information retrieval
Rocchio, J. J. (1971) · Medical Entomology and Zoology · Cited by 2,680
Information Retrieval: Data Structures and Algorithms
Frakes, W. B., & Baeza-Yates, R. A. (1992) · Cited by 2,393
The SMART Retrieval System—Experiments in Automatic Document Processing
Salton, G. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,252
Robertson, S. E., Walker, S., Jones, S., Hancock‐Beaulieu, M., & Gatford, M. (1994) · Text REtrieval Conference · Cited by 2,212
Inside the search process: Information seeking from the user's perspective
Kuhlthau, C. C. (1991) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199106)42:5<361::aid-asi6>3.0.co;2-# · Cited by 2,166
Cited by
A Survey of Automatic Query Expansion in Information Retrieval
Carpineto, C., & Romano, G. (2012) · ACM Computing Surveys · doi:10.1145/2071389.2071390 · Cited by 1,109
Emerging topic detection on Twitter based on temporal and social terms evaluation
Cataldi, M., Di Caro, L., & Schifanella, C. (2010) · Proceedings of the Tenth International Workshop on Multimedia Data Mining · doi:10.1145/1814245.1814249 · Cited by 537
Medelyan, O., Milne, D., Legg, C., & Witten, I. H. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.05.004 · Cited by 357
Query expansion techniques for information retrieval: a survey
Azad, H. K., & Deepak, A. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.05.009 · Cited by 313
Metadata
- Authors
- RUTHVEN, I., & LALMAS, M.
- Venue
- The Knowledge Engineering Review
- Publisher
- Maximum Academic Press
- Published
- 2003-06
- Volume
- 18
- Issue
- 2
- Pages
- 95-145
- ISSN
- 0269-8889 (ISSN-L)
- 1469-8005
- OpenAlex
- W2117829666
- Cited by
- 401
- References
- 69
- FWCI
- 28.20
- Type
- journal article
- Language
- en
- Open access
- green
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: A survey on the use of relevance feedback for information access systems. https://app.cochat.ai/papers/10.1017/s0269888903000638
- The paper
- RUTHVEN, I., & LALMAS, M. (2003). A survey on the use of relevance feedback for information access systems. The Knowledge Engineering Review. https://doi.org/10.1017/s0269888903000638
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.