Model-Based Collaborative Filtering



The neighborhood-based methods of the previous chapter can be viewed as generalizations of k-nearest neighbor classifiers, which are commonly used in machine learning.


Collaborative Filtering Neighborhood-based Methods Latent Factor Model Missing Entries Implicit Feedback 
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. [13]
    D. Agarwal, and B. Chen. Regression-based latent factor models. ACM KDD Conference, pp. 19–28. 2009.Google Scholar
  2. [18]
    C. Aggarwal. Data classification: algorithms and applications. CRC Press, 2014.Google Scholar
  3. [22]
    C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.Google Scholar
  4. [23]
    C. Aggarwal and J. Han. Frequent pattern mining. Springer, New York, 2014.Google Scholar
  5. [24]
    C. Aggarwal and S. Parthasarathy. Mining massively incomplete data sets by conceptual reconstruction. ACM KDD Conference, pp. 227–232, 2001.Google Scholar
  6. [25]
    C. Aggarwal, C. Procopiuc, and P. S. Yu. Finding localized associations in market basket data. IEEE Transactions on Knowledge and Data Engineering, 14(1), pp. 51–62, 2001.CrossRefGoogle Scholar
  7. [31]
    C. Aggarwal, Z. Sun, and P. Yu. Online generation of profile association rules. ACM KDD Conference, pp. 129–133, 1998.Google Scholar
  8. [32]
    C. Aggarwal, Z. Sun, and P. Yu. Online algorithms for finding profile association rules, CIKM Conference, pp. 86–95, 1998.Google Scholar
  9. [58]
    R. Battiti. Accelerated backpropagation learning: Two optimization methods. Complex Systems, 3(4), pp. 331–342, 1989.zbMATHGoogle Scholar
  10. [72]
    R. Bell and Y. Koren. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. IEEE International Conference on Data Mining, pp. 43–52, 2007.Google Scholar
  11. [73]
    R. Bell and Y. Koren. Lessons from the Netflix prize challenge. ACM SIGKDD Explorations Newsletter, 9(2), pp. 75–79, 2007.CrossRefGoogle Scholar
  12. [76]
    D. P. Bertsekas. Nonlinear programming. Athena Scientific Publishers, Belmont, 1999.Google Scholar
  13. [82]
    D. Billsus and M. Pazzani. Learning collaborative information filters. ICML Conference, pp. 46–54, 1998.Google Scholar
  14. [87]
    C. M. Bishop. Neural networks for pattern recognition. Oxford University Press, 1995.Google Scholar
  15. [96]
    M. Brand. Fast online SVD revisions for lightweight recommender systems. SIAM Conference on Data Mining, pp. 37–46, 2003.Google Scholar
  16. [127]
    J. Cai, E. Candes, and Z. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, 20(4), 1956–1982, 2010.Google Scholar
  17. [133]
    J. Canny. Collaborative filtering with privacy via factor analysis. ACM SIGR Conference, pp. 238–245, 2002.Google Scholar
  18. [151]
    T. Chen, Z. Zheng, Q. Lu, W. Zhang, and Y. Yu. Feature-based matrix factorization. arXiv preprint arXiv:1109.2271, 2011.Google Scholar
  19. [161]
    A. Cichocki and R. Zdunek. Regularized alternating least squares algorithms for non-negative matrix/tensor factorization. International Symposium on Neural Networks, pp. 793–802. 2007.Google Scholar
  20. [180]
    D. DeCoste. Collaborative prediction using ensembles of maximum margin matrix factorizations. International Conference on Machine Learning, pp. 249–256, 2006.Google Scholar
  21. [184]
    R. Devooght, N. Kourtellis, and A. Mantrach. Dynamic matrix factorization with priors on unknown values. ACM KDD Conference, 2015.Google Scholar
  22. [217]
    R. Gemulla, E. Nijkamp, P. Haas, and Y. Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent. ACM KDD Conference, pp. 69–77, 2011.Google Scholar
  23. [219]
    L. Getoor and M. Sahami. Using probabilistic relational models for collaborative filtering. Workshop on Web Usage Analysis and User Profiling, 1999.Google Scholar
  24. [220]
    F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 2(2), pp. 219–269, 1995.CrossRefGoogle Scholar
  25. [252]
    T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1), pp. 89–114, 2004.CrossRefGoogle Scholar
  26. [260]
    Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. IEEE International Conference on Data Mining, pp. 263–272, 2008.Google Scholar
  27. [267]
    P. Jain and I. Dhillon. Provable inductive matrix completion. arXiv preprint arXiv:1306.0626
  28. [268]
    P. Jain, P. Netrapalli, and S. Sanghavi. Low-rank matrix completion using alternating minimization. ACM Symposium on Theory of Computing, pp. 665–674, 2013.Google Scholar
  29. [300]
    D. Kim, and B. Yum. Collaborative filtering Based on iterative principal component analysis, Expert Systems with Applications, 28, pp. 623–830, 2005.Google Scholar
  30. [301]
    H. Kim and H. Park. Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 713–730, 2008.MathSciNetCrossRefzbMATHGoogle Scholar
  31. [309]
    Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. ACM KDD Conference, pp. 426–434, 2008. Extended version of this paper appears as: “Y. Koren. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD), 4(1), 1, 2010.”Google Scholar
  32. [310]
    Y. Koren. Collaborative filtering with temporal dynamics. ACM KDD Conference, pp. 447–455, 2009. Another version also appears in the Communications of the ACM,, 53(4), pp. 89–97, 2010.Google Scholar
  33. [311]
    Y. Koren. The Bellkor solution to the Netflix grand prize. Netflix prize documentation, 81, 2009.
  34. [312]
    Y. Koren and R. Bell. Advances in collaborative filtering. Recommender Systems Handbook, Springer, pp. 145–186, 2011. (Extended version in 2015 edition of handbook).Google Scholar
  35. [313]
    Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), pp. 30–37, 2009.CrossRefGoogle Scholar
  36. [321]
    S. Kabbur, X. Ning, and G. Karypis. FISM: factored item similarity models for top-N recommender systems. ACM KDD Conference, pp. 659–667, 2013.Google Scholar
  37. [322]
    S. Kabbur and G. Karypis. NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems. IEEE Data Mining Workshop (ICDMW), pp. 167–174, 2014.Google Scholar
  38. [331]
    A. Langville, C. Meyer, R. Albright, J. Cox, and D. Duling. Initializations for the nonnegative matrix factorization. ACM KDD Conference, pp. 23–26, 2006.Google Scholar
  39. [342]
    D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering. SIAM Conference on Data Mining, 2005.Google Scholar
  40. [351]
    M. Li, T. Zhang, Y. Chen, and A. Smola. Efficient mini-batch training for stochastic optimization. ACM KDD Conference, pp. 661–670, 2014.Google Scholar
  41. [357]
    C.-J. Lin. Projected gradient methods for nonnegative matrix factorization. Neural Computation, 19(10), pp. 2576–2779, 2007.MathSciNetCrossRefGoogle Scholar
  42. [358]
    W. Lin. Association rule mining for collaborative recommender systems. Masters Thesis, Worcester Polytechnic Institute, 2000.Google Scholar
  43. [359]
    W. Lin, S. Alvarez, and C. Ruiz. Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6(1), pp. 83–105, 2002.MathSciNetCrossRefGoogle Scholar
  44. [365]
    B. Liu, W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports. ACM KDD Conference, pp. 337–341, 1999.Google Scholar
  45. [371]
    X. Liu, C. Aggarwal, Y.-F. Lee, X. Kong, X. Sun, and S. Sathe. Kernelized matrix factorization for collaborative filtering. SIAM Conference on Data Mining, 2016.Google Scholar
  46. [434]
    A. Mild and M. Natter. Collaborative filtering or regression models for Internet recommendation systems?. Journal of Targeting, Measurement and Analysis for Marketing, 10(4), pp. 304–313, 2002.CrossRefGoogle Scholar
  47. [437]
    K. Miyahara, and M. J. Pazzani. Collaborative filtering with the simple Bayesian classifier. Pacific Rim International Conference on Artificial Intelligence, 2000.Google Scholar
  48. [441]
    B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Effective personalization based on association rule discovery from Web usage data. ACM Workshop on Web Information and Data Management, pp. 9–15, 2001.Google Scholar
  49. [455]
    X. Ning and G. Karypis. SLIM: Sparse linear methods for top-N recommender systems. IEEE International Conference on Data Mining, pp. 497–506, 2011.Google Scholar
  50. [457]
    D. Oard and J. Kim. Implicit feedback for recommender systems. Proceedings of the AAAI Workshop on Recommender Systems, pp. 81–83, 1998.Google Scholar
  51. [460]
    P. Paatero and U. Tapper. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5(2), pp. 111–126, 1994.CrossRefGoogle Scholar
  52. [467]
    R. Pan, Y. Zhou, B. Cao, N. Liu, R. Lukose, M. Scholz, Q. Yang. One-class collaborative filtering. IEEE International Conference on Data Mining, pp. 502–511, 2008.Google Scholar
  53. [468]
    R. Pan, and M. Scholz. Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. ACM KDD Conference, pp. 667–676, 2009.Google Scholar
  54. [472]
    S. Parthasarathy and C. Aggarwal. On the use of conceptual reconstruction for mining massively incomplete data sets. IEEE Transactions on Knowledge and Data Engineering, 15(6), pp. 1512–1521, 2003.CrossRefGoogle Scholar
  55. [473]
    A. Paterek. Improving regularized singular value decomposition for collaborative filtering. Proceedings of KDD Cup and Workshop, 2007.Google Scholar
  56. [474]
    V. Pauca, J. Piper, and R. Plemmons. Nonnegative matrix factorization for spectral data analysis. Linear algebra and its applications, 416(1), pp. 29–47, 2006.MathSciNetCrossRefzbMATHGoogle Scholar
  57. [493]
    S. Rendle. Factorization machines. IEEE International Conference on Data Mining, pp. 995–100, 2010.Google Scholar
  58. [500]
    J. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. ICML Conference, pp. 713–718, 2005.Google Scholar
  59. [517]
    R. Salakhutdinov, and A. Mnih. Probabilistic matrix factorization. Advances in Neural and Information Processing Systems, pp. 1257–1264, 2007.Google Scholar
  60. [518]
    R. Salakhutdinov, and A. Mnih. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. International Conference on Machine Learning, pp. 880–887, 2008.Google Scholar
  61. [519]
    R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted Boltzmann machines for collaborative filtering. International conference on Machine Learning, pp. 791–798, 2007.Google Scholar
  62. [524]
    B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. World Wide Web Conference, pp. 285–295, 2001.Google Scholar
  63. [525]
    B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Application of dimensionality reduction in recommender system – a case study. WebKDD Workshop at ACM SIGKDD Conference, 2000. Also appears at Technical Report TR-00-043, University of Minnesota, Minneapolis, 2000.
  64. [537]
    D. Seung, and L. Lee. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, pp. 556–562, 2001.Google Scholar
  65. [541]
    H. Shen and J. Z. Huang. Sparse principal component analysis via regularized low rank matrix approximation. Journal of multivariate analysis. 99(6), pp. 1015–1034, 2008.MathSciNetCrossRefzbMATHGoogle Scholar
  66. [552]
    M.-L. Shyu, C. Haruechaiyasak, S.-C. Chen, and N. Zhao. Collaborative filtering by mining association rules from user access sequences. Workshop on Challenges in Web Information Retrieval and Integration, pp. 128–135, 2005.Google Scholar
  67. [568]
    G. Strang. An introduction to linear algebra. Wellesley Cambridge Press, 2009.Google Scholar
  68. [569]
    N. Srebro, J. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. Advances in neural information processing systems, pp. 1329–1336, 2004.Google Scholar
  69. [571]
    X. Su, T. Khoshgoftaar, X. Zhu, and R. Greiner. Imputation-boosted collaborative filtering using machine learning classifiers. ACM symposium on Applied computing, pp. 949–950, 2008.Google Scholar
  70. [586]
    G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. Matrix factorization and neighbor based algorithms for the Netflix prize problem. ACM Conference on Recommender Systems, pp. 267–274, 2008.Google Scholar
  71. [620]
    S. Vucetic and Z. Obradovic. Collaborative filtering using a regression-based approach. Knowledge and Information Systems, 7(1), pp. 1–22, 2005.CrossRefGoogle Scholar
  72. [624]
    M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. CoFiRank: Maximum margin matrix factorization for collaborative ranking. Advances in Neural Information Processing Systems, 2007.Google Scholar
  73. [629]
    S. Wild, J. Curry, and A. Dougherty. Improving non-negative matrix factorizations through structured initialization. Pattern Recognition, 37(11), pp. 2217–2232, 2004.CrossRefGoogle Scholar
  74. [638]
    Z. Xia, Y. Dong, and G. Xing. Support vector machines for collaborative filtering. Proceedings of the 44th Annual Southeast Regional Conference, pp. 169–174, 2006.Google Scholar
  75. [650]
    H. F. Yu, C. Hsieh, S. Si, and I. S. Dhillon. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. IEEE International Conference on Data Mining, pp. 765–774, 2012.Google Scholar
  76. [651]
    K. Yu, S. Zhu, J. Lafferty, and Y. Gong. Fast nonparametric matrix factorization for large-scale collaborative filtering. ACM SIGIR Conference, pp. 211–218, 2009.Google Scholar
  77. [666]
    S. Zhang, W. Wang, J. Ford, and F. Makedon. Learning from incomplete ratings using nonnegative matrix factorization. SIAM Conference on Data Mining, pp. 549–553, 2006.Google Scholar
  78. [669]
    T. Zhang and V. Iyengar. Recommender systems using linear classifiers. Journal of Machine Learning Research, 2, pp. 313–334, 2002.zbMATHGoogle Scholar
  79. [676]
    K. Zhou, S. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. ACM SIGIR Conference, pp. 315–324, 2011.Google Scholar
  80. [677]
    Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel collaborative filtering for the Netflix prize. Algorithmic Aspects in Information and Management, pp. 337–348, 2008.Google Scholar
  81. [679]
    C. Ziegler. Applying feed-forward neural networks to collaborative filtering, Master’s Thesis, Universitat Freiburg, 2006.Google Scholar
  82. [704]

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations