The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Multistakeholder recommendation: Survey and research directions
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
(2010) · Cited by 24,023
Platform Competition in Two-Sided Markets
Rochet, J.-C., & Tirole, J. (2003) · Journal of the European Economic Association · doi:10.1162/154247603322493212 · Cited by 5,642
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
Strategic Management: A Stakeholder Approach
Freeman, R. E. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9781139192675 · Cited by 4,494
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
Social information filtering: algorithms for automating “word of mouth”
Shardanand, U., & Maes, P. (1995) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95 · doi:10.1145/223904.223931 · Cited by 2,823
GroupLens: applying collaborative filtering to Usenet news
Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997) · Communications of the ACM · doi:10.1145/245108.245126 · Cited by 2,367
Improving recommendation lists through topic diversification
Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005) · Proceedings of the 14th international conference on World Wide Web - WWW '05 · doi:10.1145/1060745.1060754 · Cited by 1,892
Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis
Roth, A. E., & Sotomayor, M. A. O. (1990) · Cambridge University Press eBooks · doi:10.1017/ccol052139015x · Cited by 1,340
Participatory Design: Issues and Concerns
Kensing, F., & Blomberg, J. (1998) · Computer Supported Cooperative Work (CSCW) · doi:10.1023/a:1008689307411 · Cited by 1,084
Cited by
A Review on Fairness in Machine Learning
Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3418487 · Cited by 183
Self-citationPalomares, I., Porcel, C., Pizzato, L., Guy, I., & Herrera-Viedma, E. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.12.001 · Cited by 109
Self-citationBreak the Loop: Gender Imbalance in Music Recommenders
Ferraro, A., Serra, X., & Bauer, C. (2021) · Proceedings of the 2021 Conference on Human Information Interaction and Retrieval · doi:10.1145/3406522.3446033 · Cited by 92
Understanding User Perspectives on Sustainability and Fairness in Tourism Recommender Systems
Banik, P., Banerjee, A., & Wörndl, W. (2023) · Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3563359.3597442 · Cited by 9
Metadata
- Authors
- Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., & Pizzato, L.
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2020-01-10
- Volume
- 30
- Issue
- 1
- Pages
- 127-158
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W3099814932
- Cited by
- 319
- References
- 101
- FWCI
- 36.73
- Type
- journal article
- 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: Multistakeholder recommendation: Survey and research directions. https://app.cochat.ai/papers/10.1007/s11257-019-09256-1
- The paper
- Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., & Pizzato, L. (2020). Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction. https://doi.org/10.1007/s11257-019-09256-1
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.
