Leveraging position bias to improve peer recommendation
Open accessAbstract
With the advent of social media and peer production, the amount of new online content has grown dramatically. To identify interesting items in the vast stream of new content, providers must rely on peer recommendation to aggregate opinions of their many users. Due to human cognitive biases, the presentation order strongly affects how people allocate attention to the available content. Moreover, we can manipulate attention through the presentation order of items to change the way peer recommendation works. We experimentally evaluate this effect using Amazon Mechanical Turk. We find that different policies for ordering content can steer user attention so as to improve the outcomes of peer recommendation.
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Research into human choice suggests that both explicit and implicit choices systematically vary based on context, especially the other options that are present when choosing.
“Research into human choice suggests that both explicit and implicit choices systematically vary based on context, especially the other options that are present when choosing [56–59].”
From Evolution and impact of bias in human and machine learning algorithm interaction (2020) · cites this paper as [59]
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Bounded Rationality in Scholarly Knowledge DiscoveryPreprint
Lerman, K., Hodas, N. O., & Wu, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.00269 · Cited by 3 · see citing works
Self-citationMeasuring and Optimizing Cultural MarketsPreprint
Abeliuk, A., Berbeglia, G., Cebrián, M., & Hentenryck, P. V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.1542
Leveraging the Contributions of the Casual Majority to Identify Appealing Web Content
Hogg, T., & Lerman, K. (2016) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v4i1.13277
Self-citationConsumers and Curators: Browsing and Voting Patterns on RedditPreprint
Glenski, M., Pennycuff, C., & Weninger, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05267
Quantifying the Impact of Cognitive Biases in Question-Answering SystemsPreprint
Burghardt, K., Hogg, T., & Lerman, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09633
Self-citationAn interdisciplinary examination of partisanship in web search
Robertson, R. E. (2021) · doi:10.17760/d20413988
Mehra, S., Jhunjhunwala, A., Chopra, J. S., Dave, A., & Kukreja, G. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4332-7.ch001
Operationalising AIS Bias ManagementPreprint
Albalkhi, R., Leppälä, J., & Padget, J. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5383231
Diversification and Fairness in Search: Two Sides of the Same Coin?
Cherumanal, S. P., Scholer, F., & Spina, D. (2025) · ACM Transactions on Information Systems · doi:10.1145/3774320
Lill, M., & Spann, M. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3774912
Performance Marketing versus klassisches Branding: Survival of the Fittest?
Steding, A., Schleßelmann, L., & Burmann, C. (2026) · Springer eBooks · doi:10.1007/978-3-658-49371-4_10
Fairness-Aware Ranking Recovery from Pairwise Comparisons
Ahnert, G., Ferrara, A., & Wagner, C. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795766.3799744
- Authors
- Lerman, K., & Hogg, T.
- Affiliations
- University of Southern California (US)
- Institute for Molecular Manufacturing (US)
- Corresponding author
- Lerman, Kristina
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-06-11
- Volume
- 9
- Issue
- 6
- Pages
- e98914
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4053364
- OpenAlex
- W2025574664
- Cited by
- 96
- References
- 22
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 16 · 2020: 5 · 2019: 7 · 2018: 15 · 2017: 16 · 2016: 12 · 2015: 5 · 2014: 2
- FWCI
- 32.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Defense Advanced Research Projects Agency (W911NF- 12-1-0034)
- Air Force Office of Scientific Research (FA9550-, FA9550-10-1-, FA9550-10-1-0569)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- peer recommendations, position bias, presentation order, user attention, cognitive biases, social media
- MeSH terms
- Attention, Humans, Peer Group, Probability, Social Media
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 ›
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