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Ensemble-Based and Hybrid Recommender Systems

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.

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Figure 6.1
Figure 6.2
Figure 6.3

Notes

  1. 1.

    Both entries were tied on the error rate. The award was given to the former because it was submitted 20 minutes earlier.

  2. 2.

    This is also referred to as a pipelined system [275].

  3. 3.

    It is possible for the unspecified values in duplicate rows to predicted differently, even though this is relatively unusual for most collaborative filtering algorithms.

  4. 4.

    The work in [67] proposes only the first technique for computing the similarity.

  5. 5.

    In the context of the Netflix Prize contest, this was achieved on a special part of the data set, referred to as the probe set. The probe set was not used for building the component ensemble models.

Bibliography

  1. 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. C. Aggarwal. Data mining: the textbook. Springer, New York, 2015.

    Google Scholar 

  3. 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. 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. 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. J. Basilico, and T. Hofmann. Unifying collaborative and content-based filtering. International Conference on Machine Learning, 2004.

    Google Scholar 

  7. 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. 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. D. Billsus and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2–3), pp. 147–180, 2000.

    CrossRef  Google Scholar 

  10. L. Breiman. Bagging predictors. Machine Learning, 24(2), pp. 123–140, 1996.

    MathSciNet  MATH  Google Scholar 

  11. P. Buhlmann. Bagging, subagging and bragging for improving some prediction algorithms, Recent advances and trends in nonparametric statistics, Elsivier, 2003.

    Google Scholar 

  12. P. Buhlmann and B. Yu. Analyzing bagging. Annals of statistics, 20(4), pp. 927–961, 2002.

    MathSciNet  MATH  Google Scholar 

  13. L. Breiman. Bagging predictors. Machine learning, 24(2), pp. 123–140, 1996.

    MathSciNet  MATH  Google Scholar 

  14. R. Burke. Hybrid recommender systems: Survey and experiments. User Modeling and User-adapted Interaction, 12(4), pp. 331–370, 2002.

    CrossRef  MATH  Google Scholar 

  15. R. Burke. Hybrid Web recommender systems. The adaptive Web, pp. 377–406, Springer, 2007.

    Google Scholar 

  16. R. Burke, K. Hammond, and B. Young. The FindMe approach to assisted browsing. IEEE Expert, 12(4), pp. 32–40, 1997.

    CrossRef  Google Scholar 

  17. 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.

    CrossRef  Google Scholar 

  18. 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. 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. D. DeCoste. Collaborative prediction using ensembles of maximum margin matrix factorizations. International Conference on Machine Learning, pp. 249–256, 2006.

    Google Scholar 

  21. 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. Y. Freund and R. Schapire. Experiments with a new boosting algorithm. ICML Conference, pp. 148–156, 1996.

    Google Scholar 

  23. 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. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. Springer, 2009.

    Google Scholar 

  25. M. Jahrer, A. Toscher, and R. Legenstein. Combining predictions for accurate recommender systems. ACM KDD Conference, pp. 693–702, 2010.

    Google Scholar 

  26. D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. An introduction to recommender systems, Cambridge University Press, 2011.

    Google Scholar 

  27. 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. J.-S. Lee and S. Olafsson. Two-way cooperative prediction for collaborative filtering recommendations. Expert Systems with Applications, 36(3), pp. 5353–5361, 2009.

    CrossRef  Google Scholar 

  29. M. Littlestone and M. Warmuth. The weighted majority algorithm. Information and computation, 108(2), pp. 212–261, 1994.

    MathSciNet  CrossRef  MATH  Google Scholar 

  30. 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. P. Melville, R. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. AAAI/IAAI, pp. 187–192, 2002.

    Google Scholar 

  32. 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. 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. M. Pazzani. A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13, (5–6), 1999.

    Google Scholar 

  35. 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. 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. 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. A. P. Singh and G. J. Gordon. Relational learning via collective matrix factorization. ACM KDD Conference, pp. 650–658, 2008.

    Google Scholar 

  39. B. Smyth and P. Cotter. A personalized television listings service. Communications of the ACM, 43(8), pp. 107–111, 2000.

    CrossRef  Google Scholar 

  40. 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. 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. 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. 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. D. H. Wolpert. Stacked generalization. Neural Networks, 5(2), pp. 241–259, 1992.

    MathSciNet  CrossRef  Google Scholar 

  45. M. Wu. Collaborative filtering via ensembles of matrix factorizations. Proceedings of the KDD Cup and Workshop, 2007.

    Google Scholar 

  46. 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. 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. 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.

    CrossRef  Google Scholar 

  49. 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. 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.

    CrossRef  Google Scholar 

  51. http://www.the-ensemble.com/

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Aggarwal, C.C. (2016). Ensemble-Based and Hybrid Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_6

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