Skip to main content

Advances in Collaborative Filtering

Abstract

The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.

Keywords

  • Root Mean Square Error
  • Recommender System
  • Prediction Rule
  • Collaborative Filter
  • Stochastic Gradient Descent

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This article includes copyrighted materials, which were reproduced with permission of ACM and IEEE. The original articles are:

R. Bell and Y. Koren, “Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights”, IEEE International Conference on Data Mining (ICDM’07), pp. 43–52, © 2007 IEEE. Reprinted by permission.

Y. Koren, “Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model”, Proc. 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, © 2008 ACM, Inc. Reprinted by permission. http://doi.acm.org/10.1145/1401890.1401944

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-0-387-85820-3_5
  • Chapter length: 42 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   179.00
Price excludes VAT (USA)
  • ISBN: 978-0-387-85820-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ali, K., and van Stam, W., “TiVo: Making Show Recommendations Using a Distributed Collaborative Filtering Architecture”, Proc. 10th ACM SIGKDD Int. Conference on Knowledge 5 Advances in Collaborative Filtering 185 Discovery and Data Mining, pp. 394–401, 2004.

    Google Scholar 

  2. Bell, R., and Koren, Y., “Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights”, IEEE International Conference on Data Mining (ICDM’07), pp. 43– 52, 2007.

    Google Scholar 

  3. Bell, R., and Koren, Y., “Lessons from the Netflix Prize Challenge”, -SIGKDD Explorations 9 (2007), 75–79.

    CrossRef  Google Scholar 

  4. Bell, R.M., Koren, Y., and Volinsky, C., “Modeling Relationships at Multiple Scales to Improve Accuracy of Large Recommender Systems”, Proc. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.

    Google Scholar 

  5. Bennet, J., and Lanning, S., “The Netflix Prize”, KDD Cup and Workshop, 2007. www.netflixprize.com.

  6. Canny, J., “Collaborative Filtering with Privacy via Factor Analysis”, Proc. 25th ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR’02), pp. 238–245, 2002.

    Google Scholar 

  7. Blei, D., Ng, A., and Jordan, M., “Latent Dirichlet Allocation”, Journal of Machine Learning Research 3 (2003), 993–1022.

    MATH  CrossRef  Google Scholar 

  8. Das, A., Datar, M., Garg, A., and Rajaram, S., “Google News Personalization: Scalable Online Collaborative Filtering”, WWW’07, pp. 271-280, 2007.

    Google Scholar 

  9. Deerwester, S., Dumais, S., Furnas, G.W., Landauer, T.K. and Harshman, R., “Indexing by Latent Semantic Analysis”, Journal of the Society for Information Science 41 (1990), 391–407.

    CrossRef  Google Scholar 

  10. Funk, S., “Netflix Update: Try This At Home”, http://sifter.org/~simon/journal/20061211.html, 2006.

  11. Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B., Bayesian Data Analysis, Chapman and Hall, 1995.

    Google Scholar 

  12. Goldberg, D., Nichols, D., Oki, B.M., and Terry, D., “Using Collaborative Filtering to Weave an Information Tapestry”, Communications of the ACM 35 (1992), 61–70.

    CrossRef  Google Scholar 

  13. Herlocker, J.L., Konstan, J.A., and Riedl, J., “Explaining Collaborative Filtering Recommendations”, Proc. ACM Conference on Computer Supported Cooperative Work, pp. 241–250, 2000.

    Google Scholar 

  14. Herlocker, J.L., Konstan, J.A., Borchers, A., and Riedl, J., “An Algorithmic Framework for Performing Collaborative Filtering”, Proc. 22nd ACM SIGIR Conference on Information Retrieval, pp. 230–237, 1999.

    Google Scholar 

  15. Hofmann, T., “Latent Semantic Models for Collaborative Filtering”, ACM Transactions on Information Systems 22 (2004), 89–115.

    CrossRef  Google Scholar 

  16. Kim, D., and Yum, B., “Collaborative Filtering Based on Iterative Principal Component Analysis”, Expert Systems with Applications 28 (2005), 823–830.

    MATH  CrossRef  Google Scholar 

  17. Koren, Y., “Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model”, Proc. 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.

    Google Scholar 

  18. Koren, Y., “Collaborative Filtering with Temporal Dynamics.” Proc. 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456, 2009.

    Google Scholar 

  19. Koren, Y., “Factor in the Neighbors: Scalable and Accurate Collaborative Filtering ”, ACM Transactions on Knowledge Discovery from Data (TKDD),4 (2010):1-24.

    CrossRef  Google Scholar 

  20. Linden, G., Smith, B., and York, J., “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, IEEE Internet Computing 7 (2003), 76–80.

    CrossRef  Google Scholar 

  21. Marlin, B.M., Zemel, R.S., Roweis, S., and Slaney, M., “Collaborative Filtering and the Missing at Random Assumption”, Proc. 23rd Conference on Uncertainty in Artificial Intelligence, 2007.

    Google Scholar 

  22. Oard, D.W.,, and Kim, J., “Implicit Feedback for Recommender Systems”, Proc. 5th DELOS Workshop on Filtering and Collaborative Filtering, pp. 31–36, 1998.

    Google Scholar 

  23. Paterek, A., “Improving Regularized Singular Value Decomposition for Collaborative Filtering”, Proc. KDD Cup and Workshop, 2007. 186 Yehuda Koren and Robert Bell

    Google Scholar 

  24. Salakhutdinov, R., Mnih, A., and Hinton, G., “Restricted Boltzmann Machines for Collaborative Filtering”, Proc. 24th Annual International Conference on Machine Learning, pp. 791– 798, 2007.

    Google Scholar 

  25. Salakhutdinov, R., and Mnih, A., “Probabilistic Matrix Factorization”, Advances in Neural Information Processing Systems 20 (NIPS’07), pp. 1257–1264, 2008.

    Google Scholar 

  26. Sarwar, B.M., Karypis, G., Konstan, J.A., and Riedl, J., “Application of Dimensionality Reduction in Recommender System – A Case Study”, WEBKDD’2000.

    Google Scholar 

  27. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., “Item-based Collaborative Filtering Recommendation Algorithms”, Proc. 10th International Conference on the World Wide Web, pp. 285-295, 2001.

    Google Scholar 

  28. Takács G., Pilászy I., Németh B. and Tikk, D., “Major Components of the Gravity Recommendation System”, SIGKDD Explorations 9 (2007), 80–84.

    CrossRef  Google Scholar 

  29. Takács G., Pilászy I., Németh B. and Tikk, D., “Matrix Factorization and Neighbor based Algorithms for the Netflix Prize Problem”, Proc. 2nd ACM conference on Recommender Systems (RecSys’08), pp.267–274, 2008.

    Google Scholar 

  30. Tintarev, N., and Masthoff, J., “A Survey of Explanations in Recommender Systems”, ICDE’07 Workshop on Recommender Systems and Intelligent User Interfaces, 2007.

    Google Scholar 

  31. Toscher, A., Jahrer, M., and Legenstein, R., “Improved Neighborhood-Based Algorithms for Large-Scale Recommender Systems”, KDD’08 Workshop on Large Scale Recommenders Systems and the Netflix Prize, 2008.

    Google Scholar 

  32. Wang, J., de Vries, A.P., and Reinders, M.J.T, “Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion”, Proc. 29th ACM SIGIR Conference on Information Retrieval, pp. 501–508, 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yehuda Koren .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Koren, Y., Bell, R. (2011). Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-85820-3_5

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-85819-7

  • Online ISBN: 978-0-387-85820-3

  • eBook Packages: Computer ScienceComputer Science (R0)