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

The DOI matches its Crossref record; 31 of the 39 references it cites were found in a registry or an index.

Advances in Collaborative Filtering

Koren, Yehuda, Rendle, Steffen, Bell, Robert·2022·Recommender Systems Handbook
DOI 10.1007/978-1-0716-2197-4_3
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1007/978-1-0716-2197-4_3

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 39 references

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

Cited by

Sorted by citations

Items 301–324 of 324

Related

See all

Metadata

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
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

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