Ensemble-Based and Hybrid Recommender Systems

  • Charu C. Aggarwal
Chapter

Abstract

In the previous chapters, we discussed three different classes of recommendation methods. Collaborative methods use the ratings of a community of users in order to make recommendations, whereas content-based methods use the ratings of a single user in conjunction with attribute-centric item descriptions to make recommendations.

Bibliography

  1. [14]
    D. Agarwal, B.-C. Chen, and B. Long. Localized factor models for multi-context recommendation. ACM KDD Conference, pp. 609–617, 2011.Google Scholar
  2. [22]
    C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.Google Scholar
  3. [65]
    X. Bao. Applying machine learning for prediction, recommendation, and integration. Ph.D dissertation, Oregon State University, 2009. http://ir.library.oregonstate.edu/xmlui/bitstream/handle/1957/12549/Dissertation_XinlongBao.pdf?sequence=1
  4. [66]
    X. Bao, L. Bergman, and R. Thompson. Stacking recommendation engines with additional meta-features. ACM Conference on Recommender Systems, pp. 109–116, 2009.Google Scholar
  5. [67]
    A. Bar, L. Rokach, G. Shani, B. Shapira, and A. Schclar. Boosting simple collaborative filtering models using ensemble methods. Arxiv Preprint, arXiv:1211.2891, 2012. Also appears in Multiple Classifier Systems, Springer, pp. 1–12, 2013. http://arxiv.org/ftp/arxiv/papers/1211/1211.2891.pdf
  6. [68]
    J. Basilico, and T. Hofmann. Unifying collaborative and content-based filtering. International Conference on Machine Learning, 2004.Google Scholar
  7. [69]
    C. Basu, H. Hirsh, and W. Cohen. Recommendation as classification: using social and content-based information in recommendation. AAAI, pp. 714–720, 1998.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. [85]
    D. Billsus and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2–3), pp. 147–180, 2000.CrossRefGoogle Scholar
  10. [99]
    L. Breiman. Bagging predictors. Machine Learning, 24(2), pp. 123–140, 1996.MathSciNetMATHGoogle Scholar
  11. [111]
    P. Buhlmann. Bagging, subagging and bragging for improving some prediction algorithms, Recent advances and trends in nonparametric statistics, Elsivier, 2003.Google Scholar
  12. [112]
    P. Buhlmann and B. Yu. Analyzing bagging. Annals of statistics, 20(4), pp. 927–961, 2002.MathSciNetMATHGoogle Scholar
  13. [113]
    L. Breiman. Bagging predictors. Machine learning, 24(2), pp. 123–140, 1996.MathSciNetMATHGoogle Scholar
  14. [117]
    R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, 12(4), pp. 331–370, 2002.CrossRefMATHGoogle Scholar
  15. [118]
    R. Burke. Hybrid Web recommender systems. The adaptive Web, pp. 377–406, Springer, 2007.Google Scholar
  16. [121]
    R. Burke, K. Hammond, and B. Young. The FindMe approach to assisted browsing. IEEE Expert, 12(4), pp. 32–40, 1997.CrossRefGoogle Scholar
  17. [129]
    L. M. de Campos, J. Fernandez-Luna, J. Huete, and M. Rueda-Morales. Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, 51(7), pp. 785–799, 2010.CrossRefGoogle Scholar
  18. [162]
    M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. Proceedings of the ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, 1999.Google Scholar
  19. [166]
    M. Condliff, D. Lewis, D. Madigan, and C. Posse. Bayesian mixed-effects models for recommender systems. ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, pp. 23–30, 1999.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. [206]
    Y. Freund, and R. Schapire. A decision-theoretic generalization of online learning and application to boosting. Computational Learning Theory, pp. 23–37, 1995.Google Scholar
  22. [207]
    Y. Freund and R. Schapire. Experiments with a new boosting algorithm. ICML Conference, pp. 148–156, 1996.Google Scholar
  23. [238]
    A. Gunawardana and C. Meek. A unified approach to building hybrid recommender systems. ACM Conference on Recommender Systems, pp. 117–124, 2009.Google Scholar
  24. [242]
    T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.Google Scholar
  25. [266]
    M. Jahrer, A. Toscher, and R. Legenstein. Combining predictions for accurate recommender systems. ACM KDD Conference, pp. 693–702, 2010.Google Scholar
  26. [275]
    D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. An introduction to recommender systems, Cambridge University Press, 2011.Google Scholar
  27. [311]
    Y. Koren. The Bellkor solution to the Netflix grand prize. Netflix prize documentation, 81, 2009. http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
  28. [338]
    J.-S. Lee and S. Olafsson. Two-way cooperative prediction for collaborative filtering recommendations. Expert Systems with Applications, 36(3), pp. 5353–5361, 2009.CrossRefGoogle Scholar
  29. [363]
    M. Littlestone and M. Warmuth. The weighted majority algorithm. Information and computation, 108(2), pp. 212–261, 1994.MathSciNetCrossRefMATHGoogle Scholar
  30. [411]
    J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. ACM Conference on Recommender systems, pp. 165–172, 2013.Google Scholar
  31. [431]
    P. Melville, R. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. AAAI/IAAI, pp. 187–192, 2002.Google Scholar
  32. [448]
    R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. ACM Conference on Digital libraries, pp. 195–204, 2000.Google Scholar
  33. [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
  34. [475]
    M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13, (5–6), 1999.Google Scholar
  35. [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
  36. [534]
    I. Schwab, A. Kobsa, and I. Koychev. Learning user interests through positive examples using content analysis and collaborative filtering. Internal Memo, GMD, St. Augustin, Germany, 2001.Google Scholar
  37. [554]
    J. Sill, G. Takacs, L. Mackey, and D. Lin. Feature-weighted linear stacking. arXiv preprint, arXiv:0911.0460, 2009. http://arxiv.org/pdf/0911.0460.pdf
  38. [557]
    A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. ACM KDD Conference, pp. 650–658, 2008.Google Scholar
  39. [559]
    B. Smyth and P. Cotter. A personalized television listings service. Communications of the ACM, 43(8), pp. 107–111, 2000.CrossRefGoogle Scholar
  40. [600]
    R. Torres, S. M. McNee, M. Abel, J. Konstan, and J. Riedl. Enhancing digital libraries with TechLens+. ACM/IEEE-CS Joint Conference on Digital libraries, pp. 228–234, 2004.Google Scholar
  41. [601]
    T. Tran and R. Cohen. Hybrid recommender systems for electronic commerce. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS-00-04, pp. 73–83, 2000.Google Scholar
  42. [610]
    M. van Satten. Supporting people in finding information: Hybrid recommender systems and goal-based structuring. Ph.D. Thesis, Telemetica Instituut, University of Twente, Netherlands, 2005.Google Scholar
  43. [623]
    A. M. Ahmad Wasfi. Collecting user access patterns for building user profiles and collaborative filtering. International Conference on Intelligent User Interfaces, pp. 57–64, 1998.Google Scholar
  44. [634]
    D. H. Wolpert. Stacked generalization. Neural Networks, 5(2), pp. 241–259, 1992.MathSciNetCrossRefGoogle Scholar
  45. [637]
    M. Wu. Collaborative filtering via ensembles of matrix factorizations. Proceedings of the KDD Cup and Workshop, 2007.Google Scholar
  46. [652]
    K. Yu, A. Shcwaighofer, V. Tresp, W.-Y. Ma, and H. Zhang. Collaborative ensemble learning. combining collaborative and content-based filtering via hierarchical Bayes, Conference on Uncertainty in Artificial Intelligence, pp. 616–623, 2003.Google Scholar
  47. [658]
    F. Zaman and H. Hirose. Effect of subsampling rate on subbagging and related ensembles of stable classifiers. Lecture Notes in Computer Science, Springer, Volume 5909, pp. 44–49, 2009.Google Scholar
  48. [659]
    M. Zanker and M. Jessenitschnig. Case studies on exploiting explicit customer requirements in recommender systems. User Modeling and User-Adapted Interaction, 19(1–2), pp. 133–166, 2009.CrossRefGoogle Scholar
  49. [660]
    M. Zanker, M. Aschinger, and M. Jessenitschnig. Development of a collaborative and constraint-based web configuration system for personalized bundling of products and services. Web Information Systems Engineering–WISE, pp. 273–284, 2007.Google Scholar
  50. [661]
    M. Zanker, M. Aschinger, and M. Jessenitschnig. Constraint-based personalised configuring of product and service bundles. International Journal of Mass Customisation, 3(4), pp. 407–425, 2010.CrossRefGoogle Scholar
  51. [704]

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

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