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Cited by 324Recommender Systems and TechniquesAdvanced Bandit Algorithms ResearchInformation Retrieval and Search Behavior

Advances in Collaborative Filtering

Koren, Yehuda, Rendle, Steffen, Bell, Robert·2022·Recommender Systems Handbook
DOI 10.1007/978-1-0716-2197-4_3
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Crossref · 10.1007/978-1-0716-2197-4_3

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31 of 31 references verified0 citation errors · 0 not checkable

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  • Collaborative filtering algorithms detect hidden patterns between items and users, achieving recommendations about novel items to similar users without using additional exogenous information about individual items or users.

    “Concurrently, collaborative filtering algorithms detect hidden patterns between items and users, achieving recommendations about novel items to similar users without using additional exogenous information about individual items or users 119”

    From Machine culture (2023) · cites this paper as [119]

Cites

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8 of the 39 references couldn’t be matched to a known paper.

Research works printed but not linked: 28

  1. D. Agarwal, B.-C. Chen, Regression-based latent factor models, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’09 (Association for Computing Machinery, New York, 2009)
  2. R. Bell, Y. Koren, C. Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’07 (Association for Computing Machinery, New York, 2007), pp. 95–104
  3. D.M. Blei, A.Y. Ng, M.I. Jordan, Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
  4. J. Canny, Collaborative filtering with privacy via factor analysis, in Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’02 (Association for Computing Machinery, New York, 2002), pp. 238–245
  5. C. Chen, D. Li, Q. Lv, J. Yan, S.M. Chu, L. Shang, MPMA: mixture probabilistic matrix approximation for collaborative filtering, in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI’16 (AAAI Press, Palo Alto, 2016), pp. 1382–1388
  6. P. Covington, J. Adams, E. Sargin, Deep neural networks for youtube recommendations, in Proceedings of the 10th ACM Conference on Recommender Systems, RecSys’16 (Association for Computing Machinery, New York, 2016), pp. 191–198
  7. A.S. Das, M. Datar, A. Garg, S. Rajaram, Google news personalization: scalable online collaborative filtering, in Proceedings of the 16th International Conference on World Wide Web, WWW’07 (Association for Computing Machinery, New York, 2007), pp. 271–280
  8. C. Freudenthaler, L. Schmidt-Thieme, S. Rendle, Bayesian factorization machines, in Proceedings of the NIPS Workshop on Sparse Representation and Low-rank Approximation (2011)
  9. S. Funk, Netflix update: try this at home (2006). http://sifter.org/~simon/journal/20061211.html
  10. J.L. Herlocker, J.A. Konstan, A. Borchers, J. Riedl, An algorithmic framework for performing collaborative filtering, in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’99 (Association for Computing Machinery, New York, 1999), pp. 230–237
  11. J.L. Herlocker, J.A. Konstan, J. Riedl, Explaining collaborative filtering recommendations, in Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, CSCW’00 (Association for Computing Machinery, New York, 2000), pp. 241–250
  12. Y. Koren, Factorization meets the neighborhood: a multifaceted collaborative filtering model, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’08 (Association for Computing Machinery, New York, 2008), pp. 426–434
  13. Y. Koren, Collaborative filtering with temporal dynamics, in Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2009), pp. 447–456
  14. J. Lee, S. Kim, G. Lebanon, Y. Singer, Local low-rank matrix approximation, in Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, ICM’13. JMLR.org. (2013), pp. II–82–II–90
  15. D. Li, C. Chen, Q. Lv, J. Yan, L. Shang, S.M. Chu, Low-rank matrix approximation with stability, in Proceedings of the 33rd International Conference on Machine Learning - Volume 48, ICML’16. JMLR.org (2016), pp. 295–303
  16. D. Liang, R.G. Krishnan, M.D. Hoffman, T. Jebara, Variational autoencoders for collaborative filtering, in Proceedings of the 2018 World Wide Web Conference, WWW’18, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee (2018)
  17. B.M. Marlin, R.S. Zemel, S. Roweis, M. Slaney, Collaborative filtering and the missing at random assumption, in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, UAI’07 (AUAI Press, Arlington, 2007), pp. 267–275
  18. D.W. Oard, J. Kim, Implicit feedback for recommender systems, in Proceedings of 5th DELOS Workshop on Filtering and Collaborative Filtering (1998), pp. 31–36
  19. A. Paterek, Improving regularized singular value decomposition for collaborative filtering, in Proceedings of KDD Cup and Workshop (2007)
  20. S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI’09 (AUAI Press, Arlington, 2009), pp. 452–461
  21. S. Rendle, L. Zhang, Y. Koren, On the difficulty of evaluating baselines: a study on recommender systems. CoRR, abs/1905.01395 (2019) · arXiv
  22. R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, in Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS’07 (Curran Associates, Red Hook, 2007), pp. 1257–1264
  23. R. Salakhutdinov, A. Mnih, G. Hinton, Restricted boltzmann machines for collaborative filtering, in Proceedings of the 24th International Conference on Machine Learning, ICML’07 (Association for Computing Machinery, New York, 2007), pp. 791–798
  24. S. Sedhain, A.K. Menon, S. Sanner, L. Xie, AutoRec: autoencoders meet collaborative filtering, in Proceedings of the 24th International Conference on World Wide Web, WWW’15 Companion (ACM, New York, 2015), pp. 111–112
  25. G. Takács, I. Pilászy, B. Németh, D. Tikk, Matrix factorization and neighbor based algorithms for the netflix prize problem, in Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys’08 (Association for Computing Machinery, New York, 2008), pp. 267–274
  26. A. Töscher, M. Jahrer, R. Legenstein, Improved neighborhood-based algorithms for large-scale recommender systems, in Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, NETFLIX’08 (Association for Computing Machinery, New York, 2008)
  27. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, in Advances in Neural Information Processing Systems, 30, ed. by I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett. (Curran Associates, Red Hook, 2017), pp. 5998–6008
  28. Y. Zheng, B. Tang, W. Ding, H. Zhou, A neural autoregressive approach to collaborative filtering, in Proceedings of the 33rd International Conference on Machine Learning - Volume 48, ICML’16, JMLR.org (2016). pp. 764–773

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Related

Authors
Koren, Y., Rendle, S., & Bell, R.
Affiliations
  • Google (Israel) (IL)
  • Google (United States) (US)
Corresponding author
Koren, Yehuda
ORCID
Venue
Recommender Systems Handbook
Publisher
Springer US
Published
2021-11-22
Pages
91-142
DOI
10.1007/978-1-0716-2197-4_3
OpenAlex
W4226210383
Cited by
324
References
39
Citations by year
2026: 38 · 2025: 99 · 2024: 83 · 2023: 78 · 2022: 20 · 2021: 2 · 2020: 2 · 2016: 2
FWCI
121.56
Type
book chapter
Language
en
Open access
closed
Licence
Locations
Springer eBooks (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
collaborative filtering, recommender systems, matrix factorization, temporal models, implicit feedback, rating prediction, Netflix Prize, cold-start recommendation, context-aware recommendation

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