The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Propagation of trust and distrust
Abstract
A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts/distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting.
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
The Market for "Lemons": Quality Uncertainty and the Market Mechanism
Akerlof, G. A. (1970) · The Quarterly Journal of Economics · doi:10.2307/1879431 · Cited by 23,016
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Judgment under Uncertainty: Heuristics and Biases
(1982) · doi:10.1017/cbo9780511809477 · Cited by 8,240
Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000) · Communications of the ACM · doi:10.1145/355112.355122 · Cited by 2,190 · cited 1×
“There have been a number of proposed models and empirical studies of the EBay trust model [12, 16, 17, 20, 22, 19].”
Background
Cited by
Internet Research in Psychology
Gosling, S. D., & Mason, W. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015321 · Cited by 458
Inferring social ties across heterogenous networks
Tang, J., Lou, T., & Kleinberg, J. (2012) · Proceedings of the fifth ACM international conference on Web search and data mining · doi:10.1145/2124295.2124382 · Cited by 289
Nadelson, L., Jorcyk, C., Yang, D., Jarratt Smith, M., Matson, S., Cornell, K., & Husting, V. (2014) · School Science and Mathematics · doi:10.1111/ssm.12051 · Cited by 235
Is distrust the negation of trust?: the value of distrust in social media
Tang, J., Hu, X., & Liu, H. (2014) · Proceedings of the 25th ACM conference on Hypertext and social media · doi:10.1145/2631775.2631793 · Cited by 91
Enhancing credibility judgment of web search results
Yamamoto, Y., & Tanaka, K. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1978942.1979126 · Cited by 75
Li, Z. (., Fang, X., & Sheng, O. R. L. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3131782 · Cited by 65
Empirically understanding trust in medical technology
Montague, E. N. H., Kleiner, B. M., & Winchester, W. W. (2009) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2009.01.004 · Cited by 64
Tang, J., & Liu, H. (2015) · Synthesis Lectures on Information Security, Privacy, and Trust · doi:10.2200/s00657ed1v01y201507spt013 · Cited by 53
Trust and Economic Organization
Cook, K. S., & State, B. (2015) · Emerging Trends in the Social and Behavioral Sciences · doi:10.1002/9781118900772.etrds0370 · Cited by 45
Keuchenius, A., Törnberg, P., & Uitermark, J. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256696 · Cited by 27
Trust Judgment in Knowledge Provenance
Huang, J., & Fox, M. S. (2006) · 16th International Workshop on Database and Expert Systems Applications (DEXA'05) · doi:10.1109/dexa.2005.193 · Cited by 3
Sentiment analysis of scientific citations
Athar, A. (2014) · Computer Laboratory, University of Cambridge · doi:10.48456/tr-856
Metadata
- Authors
- Guha, R., Kumar, R., Raghavan, P., & Tomkins, A.
- Venue
- Proceedings of the 13th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2004-05-17
- Pages
- 403-412
- OpenAlex
- W2144780381
- Cited by
- 1,464
- References
- 4
- FWCI
- 93.71
- 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
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.