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Theory and Methods

Personalized Prediction and Sparsity Pursuit in Latent Factor Models

Pages 241-252 | Received 01 Jul 2013, Published online: 05 May 2016

REFERENCES

  • Agarwal, D., and Chen, B.C. (2009), “Regression-Based Latent Factor Models,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 19–28.
  • Breese, J., Heckerman, D., and Kadie, C. (1998), “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” in Proceedings of 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52.
  • Candès, E.J., Romberg, J.K., and Tao, T. (2006), “Stable Signal Recovery From Incomplete and Inaccurate Measurements,” Communications on Pure and Applied Mathematics, 59, 1207–1223.
  • Chen, B., He, S., Li, Z., and Zhang, S. (2012), “Maximum Block Improvement and Polynomial Optimization,” SIAM Journal on Optimization, 22, 87–107.
  • Chen, S., Donoho, D., and Saunders, M. (1998), Automatic Decomposition by Basis Pursuit,” SIAM Review, 43, 129–159.
  • Das, A., Dalar, M., and Garg, A. (2007), “Google News Personalization: Scalable Online Collaborative Filtering,” in Proceedings of the 16th International Conference on World Wide Web, pp. 271–280.
  • Dean, J., and Ghemawat, S. (2008), “MapReduce: Simplified Data Processing on Large Clusters,” Communications of the ACM, 51, 107–113.
  • Forbes, P., and Zhu, M. (2011), “Content-Boosted Matrix Factorization for Recommender Systems: Experiments With Recipe Recommendation,” in Proceedings of the 5th ACM Conference on Recommender Systems, pp. 261–264.
  • Lee, T.Q., and Park, Y. (2012), “A Time-Based Recommender System Using Implicit Feedback,” in Proceeding of the 22nd International World Wide Web Conference, Lyon, France.
  • Little, J.A., and Rubin, D. (2002), Statistical Analysis With Missing Data (2nd ed.), Hoboken, NJ: Wiley.
  • Liu, J., and Ye, J. (2009), “Efficient Euclidean Projection in Linear Time,” in Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 657–664.
  • Park, S., Pennock, D., Madani, O., Good, N., and DeCoste, D. (2006), “Naive Filterbots for Robust Cold-Start Recommendations,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 699–705.
  • Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2009), “Online Dictionary Learning for Sparse Coding,” in Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689–696.
  • Mazumder, R., Hastie, T., and Tibshirani, R. (2010), “Spectral Regularization Algorithms for Learning Large Incomplete Matrices,” The Journal of Machine Learning Research, 11, 2287–2322.
  • McCullagh, P., and Nelder, J.A. (1990), Generalized Linear Model (2nd ed.), London: Chapman and Hall.
  • Nguyen, J., and Zhu, M. (2012), “Content-Boosted Matrix Factorization Techniques for Recommender Systems,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 6, 286–301.
  • Shen, X., Pan, W., and Zhu, Y. (2012), “Likelihood-Based Selection and Sharp Parameter Estimation,” Journal of the American Statistical Association, 107, 223–232.
  • Srebro, N., Alon, N., and Jaakkola, T. (2005), “Generalization Error Bounds for Collaborative Prediction With Low-Rank Matrices,” in Advances in Neural Information Processing Systems, (Vol. 17), Cambridge, MA: MIT Press, pp. 5–27.
  • Srebro, N., Rennie, J., and Jaakkola, T. (2005), “Maximum-Margin Matrix Factorization,” in Advances in Neural Information Processing Systems, (Vol. 17), Cambridge, MA: MIT Press, pp. 1329–1336.
  • Takács, G., Pilászy, I., Németh, B., and Tikk, D. (2009), “Scalable Collaborative Filtering Approaches for Large Recommender Systems,” Journal of Machine Learning Research, 10, 623–656.
  • Tipping, M.E., and Bishop, C.M. (1999), “Probabilistic Principal Component Analysis,” Journal of the Royal Statistical Society, Series B, 61, 611–622.
  • Van Meteren, R., and Van Someren, M. (2000), “Using Content-Based Filtering for Recommendation,” in Proceedings Machine Learning in New Information Age MLnet–ECML2000 Workshop, pp. 312–321.
  • Zhou, Y., Wilkinson, D., Schreiber, R., and Pan, R. (2008), “Large-Scale Collaborative Filtering for the Netflix Prize,” in Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management, pp. 337–348.

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