Recommender Systems: A Review-Reference-Cited by-同舟云学术

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Recommender Systems: A Review

Published:2023-11-06 Issue: Volume: Page:1-13
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

LeBlanc Patrick M.¹,Banks David¹,Fu Linhui¹,Li Mingyan²,Tang Zhengyu¹,Wu Qiuyi³

Affiliation:

1. Department of Statistical Science, Duke University, Durham, NC

2. Department of Mathematics and Statistics, University of North Carolina-Greensboro, Greensboro, NC

3. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2023.2279695

Reference120 articles.

1. Personalization technologies

2. “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,”;Adomavicius G.;IEEE Transactions on Knowledge and Data Engineering,2005

3. Aggarwal, C. C. (2016), Recommender Systems - The Textbook, Cham: Springer.

4. Amatriain, X., Pujol, J. M., Tintarev, N., and Oliver, N. (2009), “Rate It Again: Increasing Recommendation Accuracy by User Re-rating,” in Proceedings of the Third ACM Conference on Recommender Systems’, RecSys ’09, pp. 173–180, New York: Association for Computing Machinery. DOI: 10.1145/1639714.1639744.

5. Fab

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