Neighborhood-Based Collaborative Filtering

  • Charu C. Aggarwal


Neighborhood-based collaborative filtering algorithms, also referred to as memory-based algorithms, were among the earliest algorithms developed for collaborative filtering. These algorithms are based on the fact that similar users display similar patterns of rating behavior and similar items receive similar ratings. There are two primary types of neighborhood-based algorithms:


Target Item Rating Matrix Target User Offline Phase Latent Factor Model 
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. [17]
    C. Aggarwal. Social network data analytics. Springer, New York, 2011.Google Scholar
  2. [22]
    C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.Google Scholar
  3. [24]
    C. Aggarwal and S. Parthasarathy. Mining massively incomplete data sets by conceptual reconstruction. ACM KDD Conference, pp. 227–232, 2001.Google Scholar
  4. [33]
    C. Aggarwal, J. Wolf, K.-L. Wu, and P. Yu. Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. ACM KDD Conference, pp. 201–212, 1999.Google Scholar
  5. [49]
    C. Anderson. The long tail: why the future of business is selling less of more. Hyperion, 2006.Google Scholar
  6. [51]
    F. Aiolli. Efficient top-n recommendation for very large scale binary rated datasets. ACM conference on Recommender Systems, pp. 273–280, 2013.Google Scholar
  7. [71]
    R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. ACM KDD Conference, pp. 95–104, 2007.Google Scholar
  8. [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
  9. [98]
    J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. Conference on Uncertainty in Artificial Inetlligence, 1998.Google Scholar
  10. [146]
    S. Chee, J. Han, and K. Wang. Rectree: An efficient collaborative filtering method. Data Warehousing and Knowledge Discovery, pp. 141–151, 2001.Google Scholar
  11. [159]
    E. Christakopoulou and G. Karypis. HOSLIM: Higher-order sparse linear method for top-n recommender systems. Advances in Knowledge Discovery and Data Mining, pp. 38–49, 2014.Google Scholar
  12. [163]
    W. Cohen, R. Schapire and Y. Singer. Learning to order things. Advances in Neural Information Processing Systems, pp. 451–457, 2007.Google Scholar
  13. [167]
    M. O’Connor and J. Herlocker. Clustering items for collaborative filtering. Proceedings of the ACM SIGIR workshop on recommender systems, Vol 128. 1999.Google Scholar
  14. [173]
    P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. RecSys, pp. 39–46, 2010.Google Scholar
  15. [181]
    M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1), pp. 143–177, 2004.CrossRefGoogle Scholar
  16. [183]
    C. Desrosiers and G. Karypis. A comprehensive survey of neighborhood-based recommendation methods. Recommender Systems Handbook, pp. 107–144, 2011.Google Scholar
  17. [204]
    F. Fouss, A. Pirotte, J. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3), pp. 355–369, 2007.CrossRefGoogle Scholar
  18. [205]
    F. Fouss, L. Yen, A. Pirotte, and M. Saerens. An experimental investigation of graph kernels on a collaborative recommendation task. IEEE International Conference on Data Mining (ICDM), pp. 863–868, 2006.Google Scholar
  19. [228]
    K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2), pp. 133–151, 2001.CrossRefzbMATHGoogle Scholar
  20. [232]
    M. Gori and A. Pucci. Itemrank: a random-walk based scoring algorithm for recommender engines. IJCAI Conference, pp. 2766–2771, 2007.Google Scholar
  21. [242]
    T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.Google Scholar
  22. [245]
    J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. ACM SIGIR Conference, pp. 230–237, 1999.Google Scholar
  23. [247]
    J. Herlocker, J. Konstan,, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5(4), pp. 287–310, 2002.CrossRefGoogle Scholar
  24. [252]
    T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), 22(1), pp. 89–114, 2004.CrossRefGoogle Scholar
  25. [258]
    A. Howe, and R. Forbes. Re-considering neighborhood-based collaborative filtering parameters in the context of new data. Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 1481–1482, 2008.Google Scholar
  26. [261]
    Z. Huang, X. Li, and H. Chen. Link prediction approach to collaborative filtering. ACM/IEEE-CS joint conference on Digital libraries, pp. 141–142, 2005.Google Scholar
  27. [262]
    Z. Huang, H. Chen, and D. Zheng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22(1), pp. 116–142, 2004.CrossRefGoogle Scholar
  28. [280]
    R. Jin, J. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. ACM SIGIR Conference, pp. 337–344, 2004.Google Scholar
  29. [281]
    R. Jin, L. Si, and C. Zhai. Preference-based graphic models for collaborative filtering. Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, pp. 329–336, 2003.Google Scholar
  30. [282]
    R. Jin, L. Si, C. Zhai, and J. Callan. Collaborative filtering with decoupled models for preferences and ratings. ACM CIKM Conference, pp. 309–316, 2003.Google Scholar
  31. [299]
    M. Kendall and J. Gibbons. Rank correlation methods. Charles Griffin, 5th edition, 1990.Google Scholar
  32. [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
  33. [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
  34. [313]
    Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8), pp. 30–37, 2009.CrossRefGoogle Scholar
  35. [342]
    D. Lemire and A. Maclachlan. Slope one predictors for online rating-based collaborative filtering. SIAM Conference on Data Mining, 2005.Google Scholar
  36. [347]
    M. Levy and K. Jack. Efficient Top-N Recommendation by Linear Regression. Large Scale Recommender Systems Workshop (LSRS) at RecSys, 2013.Google Scholar
  37. [354]
    D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the American society for information science and technology, 58(7), pp. 1019–1031, 2007.CrossRefGoogle Scholar
  38. [360]
    G. Linden, B. Smith, and J. York. recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 7(1), pp. 76–80, 2003.Google Scholar
  39. [380]
    H. Ma, I. King, and M. Lyu. Effective missing data prediction for collaborative filtering. ACM SIGIR Conference, pp. 39–46, 2007.Google Scholar
  40. [400]
    C. Manning, P. Raghavan, and H. Schutze. Introduction to information retrieval. Cambridge University Press, Cambridge, 2008.Google Scholar
  41. [430]
    N. Meinshausen. Sign-constrained least squares estimation for high-dimensional regression. Electronic Journal of Statistics, 7, pp. 607–1631, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  42. [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
  43. [456]
    X. Ning and G. Karypis. Sparse linear methods with side information for top-n recommendations. ACM Conference on Recommender Systems, pp. 155–162, 2012.Google Scholar
  44. [463]
    Y. Park and A. Tuzhilin. The long tail of recommender systems and how to leverage it. Proceedings of the ACM Conference on Recommender Systems, pp. 11–18, 2008.Google Scholar
  45. [469]
    W. Pan and L. Chen. CoFiSet: Collaborative filtering via learning pairwise preferences over item-sets. SIAM Conference on Data Mining, 2013.Google Scholar
  46. [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
  47. [500]
    J. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. ICML Conference, pp. 713–718, 2005.Google Scholar
  48. [501]
    P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175–186, 1994.Google Scholar
  49. [517]
    R. Salakhutdinov, and A. Mnih. Probabilistic matrix factorization. Advances in Neural and Information Processing Systems, pp. 1257–1264, 2007.Google Scholar
  50. [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
  51. [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.
  52. [526]
    B. Sarwar, J. Konstan, A. Borchers, J. Herlocker, B. Miller, and J. Riedl. Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. ACM Conference on Computer Supported Cooperative Work, pp. 345–354, 1998.Google Scholar
  53. [528]
    B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. International Conference on Computer and Information Technology, 2002.Google Scholar
  54. [540]
    U. Shardanand and P. Maes. Social information filtering: algorithms for automating word of mouth. ACM Conference on Human Factors in Computing Systems, 1995.Google Scholar
  55. [568]
    G. Strang. An introduction to linear algebra. Wellesley Cambridge Press, 2009.Google Scholar
  56. [613]
    K. Verstrepen and B. Goethals. Unifying nearest neighbors collaborative filtering. ACM Conference on Recommender Systems, pp. 177–184, 2014.Google Scholar
  57. [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
  58. [622]
    J. Wang, A. de Vries, and M. Reinders. Unifying user-based and item-based similarity approaches by similarity fusion. ACM SIGIR Conference, pp. 501–508, 2006.Google Scholar
  59. [643]
    B. Xu, J. Bu, C. Chen, and D. Cai. An exploration of improving collaborative recommender systems via user-item subgroups. World Wide Web Conference, pp. 21–30, 2012.Google Scholar
  60. [644]
    G. Xue, C. Lin, Q. Yang, W. Xi, H. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. ACM SIGIR Conference, pp. 114–121, 2005.Google Scholar
  61. [647]
    H. Yildirim, and M. Krishnamoorthy. A random walk method for alleviating the sparsity problem in collaborative filtering. ACM Conference on Recommender Systems, pp. 131–138, 2008.Google Scholar
  62. [648]
    H. Yin, B. Cui, J. Li, J. Yao, and C. Chen. Challenging the long tail recommendation. Proceedings of the VLDB Endowment, 5(9), pp. 896–907, 2012.CrossRefGoogle Scholar
  63. [669]
    T. Zhang and V. Iyengar. Recommender systems using linear classifiers. Journal of Machine Learning Research, 2, pp. 313–334, 2002.zbMATHGoogle Scholar
  64. [689]

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations