Advances in Collaborative Filtering


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.


Root Mean Square Error Recommender System Prediction Rule Collaborative Filter Neighborhood Method 
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.


  1. 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 Discovery and Data Mining, pp. 394–401, 2004.Google Scholar
  2. 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. 3.
    Bell, R., and Koren, Y., “Lessons from the Netflix Prize Challenge”, SIGKDD Explorations 9 (2007), 75–79.CrossRefGoogle Scholar
  4. 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. 5.
    Bennet, J., and Lanning, S., “The Netflix Prize”, KDD Cup and Workshop, 2007.
  6. 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. 7.
    Blei, D., Ng, A., and Jordan, M., “Latent Dirichlet Allocation”, Journal of Machine Learning Research 3 (2003), 993–1022.Google Scholar
  8. 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. 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.Google Scholar
  10. 10.
    Funk, S., “Netflix Update: Try This At Home”,, 2006.
  11. 11.
    Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B., Bayesian Data Analysis, Chapman and Hall, 1995.Google Scholar
  12. 12.
    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
  13. 13.
    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
  14. 14.
    Hofmann, T., “Latent Semantic Models for Collaborative Filtering”, ACM Transactions on Information Systems 22 (2004), 89–115.Google Scholar
  15. 15.
    Kim, D., and Yum, B., “Collaborative Filtering Based on Iterative Principal Component Analysis”, Expert Systems with Applications 28 (2005), 823–830.Google Scholar
  16. 16.
    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
  17. 17.
    Koren, Y., “Factor in the Neighbors: Scalable and Accurate Collaborative Filtering ”, ACM Transactions on Knowledge Discovery from Data (TKDD),4(2010):1–24.Google Scholar
  18. 18.
    Linden, G., Smith, B., and York, J., “ Recommendations: Item-to-Item Collaborative Filtering”, IEEE Internet Computing 7 (2003), 76–80.Google Scholar
  19. 19.
    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
  20. 20.
    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
  21. 21.
    Paterek, A., “Improving Regularized Singular Value Decomposition for Collaborative Filtering”, Proc. KDD Cup and Workshop, 2007.Google Scholar
  22. 22.
    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
  23. 23.
    Salakhutdinov, R., and Mnih, A., “Probabilistic Matrix Factorization”, Advances in Neural Information Processing Systems 20 (NIPS’07), pp. 1257–1264, 2008.Google Scholar
  24. 24.
    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
  25. 25.
    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
  26. 26.
    Takács G., Pilászy I., Németh B. and Tikk, D., “Major Components of the Gravity Recommendation System”, SIGKDD Explorations 9 (2007), 80–84.CrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Google ResearchMountain ViewUSA
  2. 2.AT&T Labs – ResearchMiddletownUSA

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