Data Mining Methods for Recommender Systems

  • Xavier Amatriain
  • Alejandro Jaimes*
  • Nuria Oliver
  • Josep M. Pujol


In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.


Support Vector Machine Decision Tree Bayesian Network Association Rule Recommender System 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This chapter has been written with partial support of an ICREA grant from the Generalitat de Catalunya.


  1. 1.
    Adomavicius, G.,and Tuzhilin, A., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6): 734–749, 2005.CrossRefGoogle Scholar
  2. 2.
    Agrawal, R.,and Srikant, R., Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, 1994.Google Scholar
  3. 3.
    Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., and Oliver, N., The wisdom of the few: A collaborative filtering approach based on expert opinions from the web. In Proc. of SIGIR ’09, 2009.Google Scholar
  4. 4.
    Amatriain, X., Pujol, J.M., and Oliver, N., I like it... i like it not: Evaluating user ratings noise in recommender systems. In UMAP ’09, 2009.Google Scholar
  5. 5.
    Amatriain, X., Pujol, J.M., Tintarev, N., and Oliver, N., Rate it again: Increasing recommendation accuracy by user re-rating. In Recys ’09, 2009.Google Scholar
  6. 6.
    Anderson, M., Ball, M., Boley, H., Greene, S., Howse, N., Lemire, D., and S. McGrath. Racofi: A rule-applying collaborative filtering system. In Proc. IEEE/WIC COLA’03, 2003.Google Scholar
  7. 7.
    Baets, B.D., Growing decision trees in an ordinal setting. International Journal of Intelligent Systems, 2003.Google Scholar
  8. 8.
    Banerjee, S.,and Ramanathan, K., Collaborative filtering on skewed datasets. In Proc. of WWW ’08, 2008.Google Scholar
  9. 9.
    Basu, C., Hirsh, H., and Cohen, W., Recommendation as classification: Using social and content-based information in recommendation. In In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 714–720. AAAI Press, 1998.Google Scholar
  10. 10.
    Basu, C., Hirsh, H., and Cohen, W., Recommendation as classification: Using social and content-based information in recommendation. In AAAI Workshop on Recommender Systems, 1998.Google Scholar
  11. 11.
    Bell, R.M., Koren, Y., and Volinsky, C., The bellkor solution to the netflix prize. Technical report, AT&T Labs Research, 2007.Google Scholar
  12. 12.
    Bouza, A., Reif, G., Bernstein, A., and Gall, H., Semtree: ontology-based decision tree algorithm for recommender systems. In International Semantic Web Conference, 2008.Google Scholar
  13. 13.
    Bozzon, A., Prandi, G., Valenzise, G., and Tagliasacchi, M., A music recommendation system based on semantic audio segments similarity. In Proceeding of Internet and Multimedia Systems and Applications - 2008, 2008.Google Scholar
  14. 14.
    Brand, M., Fast online svd revisions for lightweight recommender systems. In SIAM International Conference on Data Mining (SDM), 2003.Google Scholar
  15. 15.
    Breese, J., Heckerman, D., and Kadie, C., Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, page 4352, 1998.Google Scholar
  16. 16.
    Burke, R., Hybrid web recommender systems. pages 377–408. 2007.Google Scholar
  17. 17.
    Cheng, W., J. Hühn, and E. Hüllermeier. Decision tree and instance-based learning for label ranking. In ICML ’09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 161–168, New York, NY, USA, 2009. ACM.Google Scholar
  18. 18.
    Cho, Y., Kim, J., and Kim, S., A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 2002.Google Scholar
  19. 19.
    Christakou, C.,and Stafylopatis, A., A hybrid movie recommender system based on neural networks. In ISDA ’05: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, pages 500–505, 2005.Google Scholar
  20. 20.
    Cohen, W., Fast effective rule induction. In Machine Learning: Proceedings of the 12th International Conference, 1995.Google Scholar
  21. 21.
    Connor, M.,and Herlocker, J., Clustering items for collaborative filtering. In SIGIR Workshop on Recommender Systems, 2001.Google Scholar
  22. 22.
    Cover, T.,and Hart, P., Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13(1):21–27, 1967.MATHCrossRefGoogle Scholar
  23. 23.
    Cristianini, N.,and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, March 2000.Google Scholar
  24. 24.
    Deerwester, S., Dumais, S.T., Furnas, G.W., L. T. K., and Harshman, R., Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41, 1990.Google Scholar
  25. 25.
    Deshpande, M.,and Karypis, G., Item-based top-n recommendation algorithms. ACM Trans.Inf. Syst., 22(1):143–177, 2004.CrossRefGoogle Scholar
  26. 26.
    B. S. et al. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the Fifth International Conference on Computer and Information Technology, 2002.Google Scholar
  27. 27.
    K. O. et al. Context-aware svm for context-dependent information recommendation. In International Conference On Mobile Data Management, 2006.Google Scholar
  28. 28.
    P. T. et al. Introduction to Data Mining. Addison Wesley, 2005.Google Scholar
  29. 29.
    S. G. et al. Tv content recommender system. In AAAI/IAAI 2000, 2000.Google Scholar
  30. 30.
    S. H. et al. Aimed- a personalized tv recommendation system. In Interactive TV: a Shared Experience, 2007.Google Scholar
  31. 31.
    T. B. et al. A trail based internet-domain recommender system using artificial neural networks. In Proceedings of the Int. Conf. on Adaptive Hypermedia and Adaptive Web Based Systems, 2002.Google Scholar
  32. 32.
    Freund, Y., Iyer, R., Schapire, R.E., and Singer, Y., An efficient boosting algorithm for combining preferences. Mach, J., Learn. Res., 4:933–969, 2003.Google Scholar
  33. 33.
    Frey, B.J.,and Dueck, D., Clustering by passing messages between data points. Science, 307, 2007.Google Scholar
  34. 34.
    Friedman, N., Geiger, D., and Goldszmidt, M., Bayesian network classifiers. Mach. Learn., 29(2-3):131–163, 1997.MATHCrossRefGoogle Scholar
  35. 35.
    Funk, S., Netflix update: Try this at home, 2006.Google Scholar
  36. 36.
    Ghani, R.,and Fano, A., Building recommender systems using a knowledge base of product semantics. In In 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, 2002.Google Scholar
  37. 37.
    Goldberg, K., Roeder, T., Gupta, D., and Perkins, C., Eigentaste: A constant time collaborative filtering algorithm. Journal Information Retrieval, 4(2):133–151, July 2001.MATHCrossRefGoogle Scholar
  38. 38.
    Golub, G.,and Reinsch, C., Singular value decomposition and least squares solutions. Numerische Mathematik, 14(5):403–420, April 1970.MATHCrossRefMathSciNetGoogle Scholar
  39. 39.
    Gose, E., Johnsonbaugh, R., and Jost, S., Pattern Recognition and Image Analysis. Prentice Hall, 1996.Google Scholar
  40. 40.
    Guha, S., Rastogi, R., and Shim, K., Rock: a robust clustering algorithm for categorical attributes. In Proc. of the 15th Intl Conf. On Data Eng., 1999.Google Scholar
  41. 41.
    Hartigan, J.A., Clustering Algorithms (Probability & Mathematical Statistics). John Wiley & Sons Inc, 1975.Google Scholar
  42. 42.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., and Riedl, J.T., Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5–53, 2004.CrossRefGoogle Scholar
  43. 43.
    Huang, Z., Zeng, D., and Chen, H., A link analysis approach to recommendation under sparse data. In Proceedings of AMCIS 2004, 2004.Google Scholar
  44. 44.
    Isaksson, A., Wallman, M., H. Göransson, and Gustafsson, M.G., Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recognition Letters, 29:1960–1965, 2008.CrossRefGoogle Scholar
  45. 45.
    Jolliffe, I.T., Principal Component Analysis. Springer, 2002.Google Scholar
  46. 46.
    Kang, H.,and Yoo, S., Svm and collaborative filtering-based prediction of user preference for digital fashion recommendation systems. IEICE Transactions on Inf & Syst, 2007.Google Scholar
  47. 47.
    Kurucz, M., Benczur, A.A., and Csalogany, K., Methods for large scale svd with missing values. In Proceedings of KDD Cup and Workshop 2007, 2007.Google Scholar
  48. 48.
    Lathia, N., Hailes, S., and Capra, L., The effect of correlation coefficients on communities of recommenders. In SAC ’08: Proceedings of the 2008 ACM symposium on Applied computing, pages 2000–2005, New York, NY, USA, 2008. ACM.CrossRefGoogle Scholar
  49. 49.
    Lin, W.,and Alvarez, S., Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery Journal, 6(1), 2004.Google Scholar
  50. 50.
    M. R. McLaughlin and Herlocker, J.L., A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proc. of SIGIR ’04, 2004.Google Scholar
  51. 51.
    S. M. McNee, Riedl, J., and Konstan, J.A., Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI ’06: CHI ’06 extended abstracts on Human factors in computing systems, pages 1097–1101, New York, NY, USA, 2006. ACM Press.CrossRefGoogle Scholar
  52. 52.
    Miyahara, K.,and Pazzani, M.J., Collaborative filtering with the simple bayesian classifier. In Pacific Rim International Conference on Artificial Intelligence, 2000.Google Scholar
  53. 53.
    Mobasher, B., Dai, H., Luo, T., and Nakagawa, M., Effective personalization based on association rule discovery from web usage data. In Workshop On Web Information And Data Management, WIDM ’01, 2001.Google Scholar
  54. 54.
    Nikovski, D.,and Kulev, V., Induction of compact decision trees for personalized recommendation. In SAC ’06: Proceedings of the 2006 ACM symposium on Applied computing, pages 575–581, New York, NY, USA, 2006. ACM.CrossRefGoogle Scholar
  55. 55.
    M. P. Omahony. Detecting noise in recommender system databases. In In Proceedings of the International Conference on Intelligent User Interfaces (IUI06), 29th1st, pages 109–115. ACM Press, 2006.Google Scholar
  56. 56.
    Paterek, A., Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop 2007, 2007.Google Scholar
  57. 57.
    Pazzani, M.J.,and Billsus, D., Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27(3):313–331, 1997.CrossRefGoogle Scholar
  58. 58.
    Pronk, V., Verhaegh, W., Proidl, A., and Tiemann, M., Incorporating user control into recommender systems based on naive bayesian classification. In RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pages 73–80, 2007.Google Scholar
  59. 59.
    Pyle, D., Data Preparation for Data Mining. Morgan Kaufmann, second edition edition, 1999.Google Scholar
  60. 60.
    Li, B., K.Q., Clustering approach for hybrid recommender system. In Web Intelligence 03, 2003.Google Scholar
  61. 61.
    Quinlan, J.R., Induction of decision trees. Machine Learning, 1(1):81–106, March 1986.Google Scholar
  62. 62.
    Rendle, S.,and L. Schmidt-Thieme. Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In Recsys ’08: Proceedings of the 2008 ACM conference on Recommender Systems, 2008.Google Scholar
  63. 63.
    Rokach, L., Maimon, O., Data Mining with Decision Trees: Theory and Applications, World Scientific Publishing (2008).Google Scholar
  64. 64.
    Zhang, J., F.S., Ouyang, Y.,and Makedon, F., Analysis of a low-dimensional linear model under recommendation attacks. In Proc. of SIGIR ’06, 2006.Google Scholar
  65. 65.
    Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., Incremental svd-based algorithms for highly scalable recommender systems. In 5th International Conference on Computer and Information Technology (ICCIT), 2002.Google Scholar
  66. 66.
    Sarwar, B.M., Karypis, G., Konstan, J.A., and Riedl, J.T., Application of dimensionality reduction in recommender systemsa case study. In ACM WebKDD Workshop, 2000.Google Scholar
  67. 67.
    Schclar, A., Tsikinovsky, A., Rokach, L., Meisels, A., and Antwarg, L., Ensemble methods for improving the performance of neighborhood-based collaborative filtering. In RecSys ’09: Proceedings of the third ACM conference on Recommender systems, pages 261–264, New York, NY, USA, 2009. ACM.CrossRefGoogle Scholar
  68. 68.
    Smyth, B., K. McCarthy, Reilly, J., D. O‘Sullivan, L. McGinty, and Wilson, D., Case studies in association rule mining for recommender systems. In Proc. of International Conference on Artificial Intelligence (ICAI ’05), 2005.Google Scholar
  69. 69.
    Spertus, E., Sahami, M., and Buyukkokten, O., Evaluating similarity measures: A large-scale study in the orkut social network. In Proceedings of the 2005 International Conference on Knowledge Discovery and Data Mining (KDD-05), 2005.Google Scholar
  70. 70.
    Tiemann, M.,and Pauws, S., Towards ensemble learning for hybrid music recommendation. In RecSys ’07: Proceedings of the 2007 ACM conference on Recommender systems, pages 177–178, New York, NY, USA, 2007. ACM.CrossRefGoogle Scholar
  71. 71.
    Toescher, A., Jahrer, M., and Legenstein, R., Improved neighborhood-based algorithms for large-scale recommender systems. In In KDD-Cup and Workshop 08, 2008.Google Scholar
  72. 72.
    Ungar, L.H.,and Foster, D.P., Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems, 2000.Google Scholar
  73. 73.
    Witten, I.H.,and Frank, E., Data Mining: Practical Machine Learning Tools and Techniques.Morgan Kaufmann, second edition edition, 2005.Google Scholar
  74. 74.
    Wu, M., Collaborative filtering via ensembles of matrix factorizations. In Proceedings of KDD Cup and Workshop 2007, 2007.Google Scholar
  75. 75.
    Xia, Z., Dong, Y., and Xing, G., Support vector machines for collaborative filtering. In ACMSE 44: Proceedings of the 44th annual Southeast regional conference, pages 169–174, New York, NY, USA, 2006. ACM.CrossRefGoogle Scholar
  76. 76.
    Xu, J.,and Araki, K., A svm-based personal recommendation system for tv programs. In Multi-Media Modelling Conference Proceedings, 2006.Google Scholar
  77. 77.
    Xue, G., Lin,R., Yang, C., Xi, Q., Zeng, W., H.-, Yu, J., and Chen, Z., Scalable collaborative filtering using cluster-based smoothing. In Proceedings of the 2005 SIGIR, 2005.Google Scholar
  78. 78.
    Yu, K., Tresp, V., and Yu, S., A nonparametric hierarchical bayesian framework for information filtering. In SIGIR ’04, 2004.Google Scholar
  79. 79.
    Zhang, Y.,and Koren, J., Efficient bayesian hierarchical user modeling for recommendation system. In SIGIR 07, 2007.Google Scholar
  80. 80.
    Ziegler, C., McNee N., S. M., Konstan, J.A., and Lausen, G., Improving recommendation lists through topic diversification. In Proc. of WWW ’05, 2005.Google Scholar
  81. 81.
    Zurada, J., Introduction to artificial neural systems. West Publishing Co., St. Paul, MN, USA, 1992.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Xavier Amatriain
    • 1
  • Alejandro Jaimes*
    • 2
  • Nuria Oliver
    • 1
  • Josep M. Pujol
    • 1
  1. 1.Telefonica ResearchBarcelonaSpain
  2. 2.Yahoo! ResearchBarcelonaSpain

Personalised recommendations