Advertisement

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.

Keywords

Recommender Systems Netflix Prize Contest Ensemble Components Ensemble System 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.

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.MathSciNetzbMATHGoogle 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.MathSciNetzbMATHGoogle Scholar
  13. [113]
    L. Breiman. Bagging predictors. Machine learning, 24(2), pp. 123–140, 1996.MathSciNetzbMATHGoogle Scholar
  14. [117]
    R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, 12(4), pp. 331–370, 2002.CrossRefzbMATHGoogle 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.MathSciNetCrossRefzbMATHGoogle 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

Personalised recommendations