The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Overview
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Registry
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Statistics
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Claims the paper makes
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Citations
Cites
Amazon.com recommendations: item-to-item collaborative filtering
Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403
Cited by
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
Ko, H., Lee, S., Park, Y., & Choi, A. (2022) · Electronics · doi:10.3390/electronics11010141 · Cited by 658
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
Causal Intervention for Leveraging Popularity Bias in Recommendation
Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462875 · Cited by 443
Metadata
- Authors
- Jannach, D., Lerche, L., Kamehkhosh, I., & Jugovac, M.
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2015-07-25
- Volume
- 25
- Issue
- 5
- Pages
- 427-491
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W1224564842
- Cited by
- 249
- References
- 1
- FWCI
- 28.83
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. https://app.cochat.ai/papers/10.1007/s11257-015-9165-3
- The paper
- Jannach, D., Lerche, L., Kamehkhosh, I., & Jugovac, M. (2015). What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Modeling and User-Adapted Interaction. https://doi.org/10.1007/s11257-015-9165-3
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.
