Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Collaborative Filtering

  • Mohamed SarwatEmail author
  • Mohamed F. Mokbel
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80733


Social filtering


Collaborative filtering assumes a set of n users \(\mathcal {U}=\{u_1,\ldots ,u_n\}\)

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng TKDE. 2005;17(6):734–49.CrossRefGoogle Scholar
  2. 2.
    Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence; 1998.Google Scholar
  3. 3.
    Das A, Datar M, Garg A, Rajaram S. Google news personalization: scalable online collaborative filtering. In: Proceedings of the 16th International World Wide Web Conference; 2007.Google Scholar
  4. 4.
    Ekstrand MD, Ludwig M, Konstan JA, Riedl J. Rethinking the recommender research ecosystem: reproducibility, openness, and lenskit. In: Proceedings of the 5th ACM Conference on Recommender Systems; 2011.Google Scholar
  5. 5.
    Koren Y, Bell RM. Advances in collaborative filtering. In: Recommender systems handbook. Springer; 2011. p. 145–86. https://link.springer.com/book/10.1007/978-0-387-85820-3Google Scholar
  6. 6.
    Koren Y, Bell RM, Volinsky C. Matrix factorization techniques for recommender systems. IEEE Comput. 2009;42(8):30–7.CrossRefGoogle Scholar
  7. 7.
    Levandoski JJ, Ekstrand MD, Ludwig M, Eldawy A, Mokbel MF, Riedl J. Recbench: benchmarks for evaluating performance of recommender system architectures. Proc VLDB Endowment. 2011;4(11):911–20.Google Scholar
  8. 8.
    Levandoski JJ, Sarwat M, Mokbel MF, Ekstrand MD. RecStore: an extensible and adaptive framework for online recommender queries inside the database engine. In: Proceedings of the 15th International Conference on Extending Database Technology; 2012.Google Scholar
  9. 9.
    Linden G, et al. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 2003;7(1):76–80.MathSciNetCrossRefGoogle Scholar
  10. 10.
    Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 Conference on Computer Supported Cooperative Work; 1994.Google Scholar
  11. 11.
    Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference; 2001.Google Scholar
  12. 12.
    Sarwat M, Avery J, Mokbel MF. RecDB in action: recommendation made easy in relational databases. Proc VLDB Endowment. 2013;6(12):1242–5.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA
  2. 2.Department of Computer Science and EngineeringUniversity of Minnesota-Twin CitiesMinneapolisUSA