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Matrix Factorization Techniques for Recommender Systems
Abstract
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
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Private traits and attributes are predictable from digital records of human behavior
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Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654
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Che, Z., Purushotham, S., Cho, K., Sontag, D., & Liu, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24271-9 · Cited by 2,370
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Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300831 · Cited by 906
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Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022) · Proceedings of the 16th ACM Conference on Recommender Systems · doi:10.1145/3523227.3546767 · Cited by 556
Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., & He, X. (2023) · Proceedings of the 17th ACM Conference on Recommender Systems · doi:10.1145/3604915.3608857 · Cited by 469
Self-supervised learning for recommender systems: A survey
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3282907 · Cited by 413
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Jain, P., & Kar, P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000058 · Cited by 364
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Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01276-1 · Cited by 367
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Metadata
- Authors
- Koren, Y., Bell, R., & Volinsky, C.
- Venue
- Computer
- Published
- 2009-08
- Volume
- 42
- Issue
- 8
- Pages
- 30-37
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2054141820
- Cited by
- 11,673
- References
- 0
- FWCI
- 346.90
- 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 ›
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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.
