메뉴 건너뛰기




Volumn 27, Issue 2, 2012, Pages 202-231

Statistical significance of the netflix challenge

Author keywords

Collaborative filtering; Cross validation; Effective number of degrees of freedom; Empirical bayes; Ensemble methods; Gradient descent; Latent factors; Nearest neighbors; Netflix contest; Neural networks; Penalization; Prediction error; Recommender systems; Restricted Boltzmann machines; Shrinkage; Singular value decomposition

Indexed keywords


EID: 84871531231     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/11-STS368     Document Type: Article
Times cited : (83)

References (130)
  • 1
    • 84871576456 scopus 로고    scopus 로고
    • ACM SIGKDD Available at
    • ACM SIGKDD (2007). KDD Cup and Workshop 2007. Available at www.cs.uic.edu/~liub/Netflix-KDD-Cup-2007.html.
    • (2007) KDD Cup and Workshop 2007
  • 2
    • 20844435854 scopus 로고    scopus 로고
    • Towards the next generation of recommender systems: A survey of the state-ofthe-art and possible extensions
    • ADOMAVICIUS, G. and TUZHILIN, A. (2005). Towards the next generation of recommender systems: A survey of the state-ofthe-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 634-749.
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , pp. 634-749
    • Adomavicius, G.1    Tuzhilin, A.2
  • 3
    • 4043135554 scopus 로고    scopus 로고
    • Optimal predictive model selection
    • BARBIERI, M. M. and BERGER, J. O. (2004). Optimal predictive model selection. Ann. Statist. 32 870-897.
    • (2004) Ann. Statist. , vol.32 , pp. 870-897
    • Barbieri, M.M.1    Berger, J.O.2
  • 4
    • 0002167090 scopus 로고
    • Predicted squared error: A criterion for automatic model selection
    • (S. Farrow, ed.) Marcel Dekker, New York
    • BARON, A. (1984). Predicted squared error: A criterion for automatic model selection. In Self-Organizing Methods in Modeling (S. Farrow, ed.). Marcel Dekker, New York.
    • (1984) Self-Organizing Methods in Modeling
    • Baron, A.1
  • 6
    • 48949100471 scopus 로고    scopus 로고
    • Improved neighborhood-based collaborative filtering
    • ACM, New York
    • BELL, R. and KOREN, Y. (2007). Improved neighborhood-based collaborative filtering. In Proc. KDD Cup and Workshop 2007 7-14. ACM, New York.
    • (2007) Proc. KDD Cup and Workshop 2007. , pp. 7-14
    • Bell, R.1    Koren, Y.2
  • 7
    • 49749086487 scopus 로고    scopus 로고
    • Scalable collaborative filtering with jointly derived neighborhood interpolation weights
    • IEEE Computer Society, Los Alamitos, CA
    • BELL, R. and KOREN, Y. (2007). Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proc. Seventh IEEE Int. Conf. on Data Mining 43-52. IEEE Computer Society, Los Alamitos, CA.
    • (2007) Proc. Seventh IEEE Int. Conf. on Data Mining , pp. 43-52
    • Bell, R.1    Koren, Y.2
  • 8
    • 36849079891 scopus 로고    scopus 로고
    • Modeling relationships at multiple scales to improve accuracy of large recommender systems
    • Proc. 13th ACM SIGKDD Int ACM, New York
    • BELL, R., KOREN, Y. and VOLINSKY, C. (2007). Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proc. 13th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 95-104. ACM, New York.
    • (2007) Conf. on Knowledge Discovery and Data Mining. , pp. 95-104
    • Bell, R.1    Koren, Y.2    Volinsky, C.3
  • 14
    • 0011451154 scopus 로고
    • Bayesian robustness and the Stein effect
    • BERGER, J. (1982). Bayesian robustness and the Stein effect. J. Amer. Statist. Assoc. 77 358-368.
    • (1982) J. Amer. Statist. Assoc. , vol.77 , pp. 358-368
    • Berger, J.1
  • 17
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • BREIMAN, L. (1996). Bagging predictors. Machine Learning 26 123-140.
    • (1996) Machine Learning , vol.26 , pp. 123-140
    • Breiman, L.1
  • 18
    • 0000927638 scopus 로고    scopus 로고
    • Predicting multivariate responses in multiple linear regression (with discussion)
    • BREIMAN, L. and FRIEDMAN, J. H. (1997). Predicting multivariate responses in multiple linear regression (with discussion). J. Roy. Statist. Soc. Ser. B 59 3-54.
    • (1997) J. Roy. Statist. Soc. Ser. B , vol.59 , pp. 3-54
    • Breiman, L.1    Friedman, J.H.2
  • 19
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • BURGES, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 121-167.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , pp. 121-167
    • Burges, C.1
  • 21
    • 34548275795 scopus 로고    scopus 로고
    • The Dantzig selector: Statistical estimation when p is much larger than n
    • CANDES, E. and TAO, T. (2007). The Dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist. 35 2313-2351.
    • (2007) Ann. Statist. , vol.35 , pp. 2313-2351
    • Candes, E.1    Tao, T.2
  • 23
    • 0003231156 scopus 로고    scopus 로고
    • Bayes and Empirical Bayes Methods for Data Analysis
    • Chapman & Hall, London
    • CARLIN, B. P. and LOUIS, T. A. (1996). Bayes and Empirical Bayes Methods for Data Analysis. Monogr. Statist. Appl. Probab. 69. Chapman & Hall, London.
    • (1996) Monogr. Statist. Appl. Probab. , pp. 69
    • Carlin, B.P.1    Louis, T.A.2
  • 24
    • 0002632234 scopus 로고
    • An introduction to empirical Bayes data analysis
    • CASELLA, G. (1985). An introduction to empirical Bayes data analysis. Amer. Statist. 39 83-87.
    • (1985) Amer. Statist. , vol.39 , pp. 83-87
    • Casella, G.1
  • 28
    • 0001573594 scopus 로고
    • Regression, prediction and shrinkage
    • COPAS, J. B. (1983). Regression, prediction and shrinkage. J. Roy. Statist. Soc. Ser. B 45 311-354.
    • (1983) J. Roy. Statist. Soc. Ser. B , vol.45 , pp. 311-354
    • Copas, J.B.1
  • 31
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm (with discussion)
    • DEMPSTER, A. P., LAIRD, N. M. and RUBIN, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. Ser. B 39 1-38.
    • (1977) J. Roy. Statist. Soc. Ser. B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 32
    • 0343784734 scopus 로고
    • Biased versus unbiased estimation
    • EFRON, B. (1975). Biased versus unbiased estimation. Advances in Math. 16 259-277.
    • (1975) Advances in Math , vol.16 , pp. 259-277
    • Efron, B.1
  • 33
    • 84950461478 scopus 로고
    • Estimating the error rate of a prediction rule: Improvement on cross-validation
    • EFRON, B. (1983). Estimating the error rate of a prediction rule: Improvement on cross-validation. J. Amer. Statist. Assoc. 78 316-331.
    • (1983) J. Amer. Statist. Assoc. , vol.78 , pp. 316-331
    • Efron, B.1
  • 34
    • 80053264999 scopus 로고
    • How biased is the apparent error rate of a prediction rule
    • EFRON, B. (1986). How biased is the apparent error rate of a prediction rule. J. Amer. Statist. Assoc. 81 461-470.
    • (1986) J. Amer. Statist. Assoc. , vol.81 , pp. 461-470
    • Efron, B.1
  • 35
    • 0347568778 scopus 로고    scopus 로고
    • Empirical Bayes methods for combining likelihoods (with discussion)
    • EFRON, B. (1996). Empirical Bayes methods for combining likelihoods (with discussion). J. Amer. Statist. Assoc. 91 538-565.
    • (1996) J. Amer. Statist. Assoc. , vol.91 , pp. 538-565
    • Efron, B.1
  • 36
    • 4944239996 scopus 로고    scopus 로고
    • The estimation of prediction error: Covariance penalties and cross-validation (with discussion)
    • EFRON, B. (2004). The estimation of prediction error: Covariance penalties and cross-validation (with discussion). J. Amer. Statist. Assoc. 99 619-642.
    • (2004) J. Amer. Statist. Assoc. , vol.99 , pp. 619-642
    • Efron, B.1
  • 38
    • 0000310469 scopus 로고
    • Limiting the risk of Bayes and empirical Bayes estimators II. The empirical Bayes case
    • EFRON, B. and MORRIS, C. (1972). Limiting the risk of Bayes and empirical Bayes estimators. II. The empirical Bayes case. J. Amer. Statist. Assoc. 67 130-139.
    • (1972) J. Amer. Statist. Assoc. , vol.67 , pp. 130-139
    • Efron, B.1    Morris, C.2
  • 39
    • 0001401955 scopus 로고
    • Empirical Bayes on vector observations: An extension of Stein's method
    • EFRON, B. and MORRIS, C. (1972). Empirical Bayes on vector observations: An extension of Stein's method. Biometrika 59 335-347.
    • (1972) Biometrika , vol.59 , pp. 335-347
    • Efron, B.1    Morris, C.2
  • 40
    • 84949161149 scopus 로고
    • Stein's estimation rule and its competitors-an empirical Bayes approach
    • EFRON, B. and MORRIS, C. (1973). Stein's estimation rule and its competitors-an empirical Bayes approach. J. Amer. Statist. Assoc. 68 117-130.
    • (1973) J. Amer. Statist. Assoc. , vol.68 , pp. 117-130
    • Efron, B.1    Morris, C.2
  • 41
    • 0000053186 scopus 로고
    • Combining possibly related estimation problems (with discussion)
    • EFRON, B. andMORRIS, C. (1973). Combining possibly related estimation problems (with discussion). J. Roy. Statist. Soc. Ser. B 35 379-421.
    • (1973) J. Roy. Statist. Soc. Ser. B , vol.35 , pp. 379-421
    • Efron, B.1    Morris, C.2
  • 42
    • 84949161141 scopus 로고
    • Data analysis using Stein's estimator and its generalization
    • EFRON, B. and MORRIS, C. (1975). Data analysis using Stein's estimator and its generalization. J. Amer. Statist. Assoc. 70 311-319.
    • (1975) J. Amer. Statist. Assoc. , vol.70 , pp. 311-319
    • Efron, B.1    Morris, C.2
  • 43
    • 0000129805 scopus 로고
    • Stein's paradox in statistics
    • EFRON, B. and MORRIS, C. (1977). Stein's paradox in statistics. Scientific American 236 119-127.
    • (1977) Scientific American , vol.236 , pp. 119-127
    • Efron, B.1    Morris, C.2
  • 45
    • 84878031768 scopus 로고    scopus 로고
    • Statistical challenges with high dimensionality: Feature selection in knowledge discovery
    • Eur. Math. Soc., Zürich
    • FAN, J. and LI, R. (2006). Statistical challenges with high dimensionality: Feature selection in knowledge discovery. In International Congress of Mathematicians III 595-622. Eur. Math. Soc., Zürich.
    • (2006) International Congress of Mathematicians III , pp. 595-622
    • Fan, J.1    Li, R.2
  • 47
    • 85044929329 scopus 로고    scopus 로고
    • See Webb, B. (2006/2007)
    • FUNK, S. (2006/2007). See Webb, B. (2006/2007).
    • (2006/2007)
    • Funk, S.1
  • 49
    • 31344454903 scopus 로고    scopus 로고
    • Persistence in highdimensional linear predictor selection and the virtue of overparametrization
    • GREENSHTEIN, E. and RITOV, Y. (2004). Persistence in highdimensional linear predictor selection and the virtue of overparametrization. Bernoulli 10 971-988.
    • (2004) Bernoulli , vol.10 , pp. 971-988
    • Greenshtein, E.1    Ritov, Y.2
  • 56
    • 0013344078 scopus 로고    scopus 로고
    • Training products of experts by minimizing contrastive divergence
    • HINTON, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Comput. 14 1771-1800.
    • (2002) Neural Comput , vol.14 , pp. 1771-1800
    • Hinton, G.E.1
  • 57
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • HOFMANN, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. J 42 177-196.
    • (2001) Mach. Learn. J , vol.42 , pp. 177-196
    • Hofmann, T.1
  • 59
    • 3042742744 scopus 로고    scopus 로고
    • Latent semantic models for collaborative filtering
    • HOFMANN, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems 22 89-115.
    • (2004) ACM Transactions on Information Systems , vol.22 , pp. 89-115
    • Hofmann, T.1
  • 60
    • 84862271600 scopus 로고    scopus 로고
    • Latent class models for collaborative filtering
    • Morgan Kaufmann, San Francisco, CA
    • HOFMANN, T. and PUZICHA, J. (1999). Latent class models for collaborative filtering. In Proc. Int. Joint Conf. on Artificial Intelligence 2 688-693. Morgan Kaufmann, San Francisco, CA.
    • (1999) Proc. Int. Joint Conf. on Artificial Intelligence. , vol.2 , pp. 688-693
    • Hofmann, T.1    Puzicha, J.2
  • 64
    • 17844390666 scopus 로고    scopus 로고
    • Collaborative filtering based on iterative principal component analysis
    • KIM, D. and YUM, B. (2005). Collaborative filtering based on iterative principal component analysis. Expert Systems with Applications 28 823-830.
    • (2005) Expert Systems with Applications , vol.28 , pp. 823-830
    • Kim, D.1    Yum, B.2
  • 66
    • 70350647708 scopus 로고    scopus 로고
    • Collaborative filtering with temporal dynamics
    • Proc. 15th ACM SIGKDD Int CM, New York
    • KOREN, Y. (2009). Collaborative filtering with temporal dynamics. In Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 447-456. ACM, New York.
    • (2009) Conf. on Knowledge Discovery and Data Mining. , pp. 447-456
    • Koren, Y.1
  • 68
    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • KOREN, Y., BELL, R. and VOLINSKY, C. (2009). Matrix factorization techniques for recommender systems. Computer 42 (8) 30-37.
    • (2009) Computer , vol.42 , Issue.8 , pp. 30-37
    • Koren, Y.1    Bell, R.2    Volinsky, C.3
  • 69
    • 0000840124 scopus 로고
    • From Stein's unbiased risk estimates to the method of generalized cross validation
    • LI, K.-C. (1985). From Stein's unbiased risk estimates to the method of generalized cross validation. Ann. Statist. 13 1352-1377.
    • (1985) Ann. Statist , vol.13 , pp. 1352-1377
    • Li, K.-C.1
  • 70
    • 48249140327 scopus 로고    scopus 로고
    • Variational Bayesian approach to movie rating predictions
    • ACM, New York
    • LIM, Y. J. and TEH, Y. W. (2007). Variational Bayesian approach to movie rating predictions. In Proc. KDD Cup and Workshop 2007 15-21. ACM, New York.
    • (2007) Proc. KDD Cup and Workshop 2007. , pp. 15-21
    • Lim, Y.J.1    Teh, Y.W.2
  • 71
    • 84871570198 scopus 로고
    • Statistical Analysis with Missing Data. Wiley, New York. MR0890519 MALLOWS
    • LITTLE, R. J. A. and RUBIN, D. B. (1987). Statistical Analysis with Missing Data. Wiley, New York. MR0890519 MALLOWS, C. (1973). Some comments on Cp. Technometrics 15 661-675.
    • (1987) C. (1973). Some comments on Cp. Technometrics , vol.15 , pp. 661-675
    • Little, R.J.A.1    Rubin, D.B.2
  • 72
    • 0004271291 scopus 로고
    • 2nd ed. Monogr. Statist. Appl. Probab. 35. Chapman & Hall, London
    • MARITZ, J. S. and LWIN, T. (1989). Empirical Bayes Methods, 2nd ed. Monogr. Statist. Appl. Probab. 35. Chapman & Hall, London.
    • (1989) Empirical Bayes Methods
    • Maritz, J.S.1    Lwin, T.2
  • 76
    • 33846662159 scopus 로고    scopus 로고
    • Support vector machines with applications
    • MOGUERZA, J. M. and MUÑOZ, A. (2006). Support vector machines with applications. Statist. Sci. 21 322-336.
    • (2006) Statist. Sci. , vol.21 , pp. 322-336
    • Moguerza, J.M.1    Muñoz, A.2
  • 77
    • 0000902690 scopus 로고
    • The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems
    • Morgan Kaufmann, San Francisco, CA
    • MOODY, J. E. (1992). The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems. In Advances in Neural Information Processing Systems 4. Morgan Kaufmann, San Francisco, CA.
    • (1992) Advances in Neural Information Processing Systems 4
    • Moody, J.E.1
  • 78
    • 84950432453 scopus 로고
    • Parametric empirical Bayes inference: Theory and applications (with discussion)
    • MORRIS, C. N. (1983). Parametric empirical Bayes inference: Theory and applications (with discussion). J. Amer. Statist. Assoc. 78 47-65.
    • (1983) J. Amer. Statist. Assoc. , vol.78 , pp. 47-65
    • Morris, C.N.1
  • 80
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse and other variants
    • (M. I. Jordan, ed.). Kluwer
    • NEAL, R. M. and HINTON, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse and other variants. In Learning in Graphical Models (M. I. Jordan, ed.) 355-368. Kluwer.
    • (1998) In Learning in Graphical Models , pp. 355-368
    • Neal, R.M.1    Hinton, G.E.2
  • 81
    • 84871558266 scopus 로고    scopus 로고
    • Netflix Prize Leaderboard Netflix Prize Forum NETFLIX INC
    • NETFLIX INC. (2006/2010). Netflix Prize webpage: http://www. netflixprize.com/. Netflix Prize Leaderboard: http://www. netflixprize.com/leaderboard/. Netflix Prize Forum: www. netflixprize.com/community/.
    • (2006/2010)., Netflix Prize webpage
  • 84
    • 57949113756 scopus 로고    scopus 로고
    • Improving regularized singular value decomposition for collaborative filtering
    • ACM, New York
    • PATEREK, A. (2007). Improving regularized singular value decomposition for collaborative filtering. In Proc. KDD Cup and Workshop 2007 39-42. ACM, New York.
    • (2007) Proc. KDD Cup and Workshop 2007. , pp. 39-42
    • Paterek, A.1
  • 86
    • 0012253296 scopus 로고    scopus 로고
    • Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments
    • Morgan Kaufmann, San Francisco, CA
    • POPESCUL, A., UNGAR, L., PENNOCK, D. and LAWRENCE, S. (2001). Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proc. 17th Conf. on Uncertainty Artificial Intelligence. Morgan Kaufmann, San Francisco, CA. 437-444.
    • (2001) Proc. 17th Conf. on Uncertainty Artificial Intelligence. , pp. 437-444
    • Popescul, A.1    Ungar, L.2    Pennock, D.3    Lawrence, S.4
  • 89
    • 31844451557 scopus 로고    scopus 로고
    • Fast maximum margin matrix factorization for collaborative prediction
    • ACM, New York
    • RENNIE, J. D. M. and SREBRO, N. (2005). Fast maximum margin matrix factorization for collaborative prediction. In Proc. 22nd Int. Conf. on Machine Learning 713-719. ACM, New York.
    • (2005) Proc. 22nd Int. Conf. on Machine Learning. , pp. 713-719
    • Rennie, J.D.M.1    Srebro, N.2
  • 93
    • 0000033354 scopus 로고
    • An empirical Bayes approach to statistics
    • In Proc. 3rd Berkeley Sympos Univ. California Press, Berkeley I
    • ROBBINS, H. (1956). An empirical Bayes approach to statistics. In Proc. 3rd Berkeley Sympos. Math. Statist. Probab. I 157-163. Univ. California Press, Berkeley.
    • (1956) Math. Statist. Probab. , pp. 157-163
    • Robbins, H.1
  • 94
    • 0002533801 scopus 로고
    • The empirical Bayes approach to statistical decision problems
    • ROBBINS, H. (1964). The empirical Bayes approach to statistical decision problems. Ann. Math. Statist. 35 1-20.
    • (1964) Ann. Math. Statist. , vol.35 , pp. 1-20
    • Robbins, H.1
  • 95
    • 0000801059 scopus 로고
    • Some thoughts on empirical Bayes estimation
    • ROBBINS, H. (1983). Some thoughts on empirical Bayes estimation. Ann. Statist. 11 713-723.
    • (1983) Ann. Statist. , vol.11 , pp. 713-723
    • Robbins, H.1
  • 96
  • 99
    • 34547983260 scopus 로고    scopus 로고
    • Proc. 24th Int. Conf. on Machine Learning. ACM Inetrnational Conference Proceeding Series. ACM, New York
    • SALAKHUTDINOV, R., MNIH, A. and HINTON, G. (2007). Restricted Boltzmann machines for collaborative filtering. In Proc. 24th Int. Conf. on Machine Learning. ACM Inetrnational Conference Proceeding Series 227 791-798. ACM, New York.
    • (2007) Restricted Boltzmann machines for collaborative filtering , vol.227 , pp. 791-798
    • Salakhutdinov, R.1    Mnih, A.2    Hinton, G.3
  • 101
    • 3042788736 scopus 로고    scopus 로고
    • Application of dimensionality reduction in recommender system-a case study
    • ACM, New York
    • SARWAR, B., KARYPIS, G., KONSTAN, J. and RIEDL, J. T. (2000). Application of dimensionality reduction in recommender system-a case study. In Proc. ACM WebKDD Workshop. ACM, New York.
    • (2000) Proc. ACM WebKDD Workshop.
    • Sarwar, B.1    Karypis, G.2    Konstan, J.3    Riedl, J.T.4
  • 103
    • 1942516801 scopus 로고    scopus 로고
    • Weighted low-rank approximations
    • (T. Fawcett and N. Mishra, eds.) 720-727. ACM, New York
    • SREBRO, N. and JAAKKOLA, T. (2003). Weighted low-rank approximations. In Proc. Twentieth Int. Conf. on Machine Learning (T. Fawcett and N. Mishra, eds.) 720-727. ACM, New York.
    • (2003) Proc. Twentieth Int. Conf. on Machine Learning
    • Srebro, N.1    Jaakkola, T.2
  • 106
    • 0000169918 scopus 로고
    • Estimation of the mean of a multivariate normal distribution
    • STEIN, C. M. (1981). Estimation of the mean of a multivariate normal distribution. Ann. Statist. 9 1135-1151.
    • (1981) Ann. Statist. , vol.9 , pp. 1135-1151
    • Stein, C.M.1
  • 107
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictions (with discussion)
    • STONE, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion). J. Roy. Statist. Soc. Ser. B 36 111-147.
    • (1974) J. Roy. Statist. Soc. Ser. B , vol.36 , pp. 111-147
    • Stone, M.1
  • 109
    • 48349120710 scopus 로고    scopus 로고
    • Major components of the Gravity recommendation system
    • TAKACS, G., PILASZY, I., NEMETH, B. and TIKK, D. (2008). Major components of the Gravity recommendation system. SIGKDD Explorations 9 80-83.
    • (2008) SIGKDD Explorations , vol.9 , pp. 80-83
    • Takacs, G.1    Pilaszy, I.2    Nemeth, B.3    Tikk, D.4
  • 111
    • 63449123891 scopus 로고    scopus 로고
    • Matrix factorization and neighbor based algorithms for the Netflix Prize problem
    • ACM, New York
    • TAKACS, G., PILASZY, I., NEMETH, B. and TIKK, D. (2008). Matrix factorization and neighbor based algorithms for the Netflix Prize problem. In Proc. ACM Conf. on Recommender Systems 267-274. ACM, New York.
    • (2008) Proc. ACM Conf. on Recommender Systems. , pp. 267-274
    • Takacs, G.1    Pilaszy, I.2    Nemeth, B.3    Tikk, D.4
  • 112
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58 267-288.
    • (1996) J. Roy. Statist. Soc. Ser. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 118
    • 84871588671 scopus 로고    scopus 로고
    • 2nd KDD Workshop on Large Scale Recommender Systems and the Netflix Prize Competition. ACM, New York
    • TUZHILIN, A., KOREN, Y., BENNETT, C., ELKAN, C. and LEMIRE, D. (2008). Proc. 2nd KDD Workshop on Large Scale Recommender Systems and the Netflix Prize Competition. ACM, New York.
    • (2008) Proc
    • Tuzhilin, A.1    Koren, Y.2    Bennett, C.3    Elkan, C.4    Lemire, D.5
  • 120
    • 0035530906 scopus 로고    scopus 로고
    • Shrinkage and penalized likelihood as methods to improve predictive accuracy
    • VAN HOUWELINGEN, J. C. (2001). Shrinkage and penalized likelihood as methods to improve predictive accuracy. Statist. Neerlandica 55 17-34.
    • (2001) Statist. Neerlandica , vol.55 , pp. 17-34
    • Van Houwelingen, J.C.1
  • 123
    • 84871534971 scopus 로고    scopus 로고
    • 27 October 2006, 2 November 2006, 11 December 2007 and 17 August 2007. Available at
    • WEBB, B. (aka Funk, S.) (2006/2007). 'Blog' entries, 27 October 2006, 2 November 2006, 11 December 2007 and 17 August 2007. Available at http://sifter.org/~simon/journal/.
    • (aka Funk, S.) (2006/2007). 'Blog' entries
    • Webb, B.1
  • 124
    • 48249137439 scopus 로고    scopus 로고
    • Collaborative filtering via ensembles of matrix factorizations
    • ACM, New York
    • WU, M. (2007). Collaborative filtering via ensembles of matrix factorizations. In Proc. KDD Cup and Workshop 2007 43-47. ACM, New York.
    • (2007) Proc. KDD Cup and Workshop 2007. , pp. 43-47
    • Wu, M.1
  • 125
    • 0032351389 scopus 로고    scopus 로고
    • On measuring and correcting the effects of data mining and model selection
    • YE, J. (1998). On measuring and correcting the effects of data mining and model selection. J. Amer. Statist. Assoc. 93 120-131.
    • (1998) J. Amer. Statist. Assoc. , vol.93 , pp. 120-131
    • Ye, J.1
  • 126
    • 29144459062 scopus 로고    scopus 로고
    • Efficient empirical Bayes variable selection and estimation in linear models
    • YUAN, M. and LIN, Y. (2005). Efficient empirical Bayes variable selection and estimation in linear models. J. Amer. Statist. Assoc. 100 1215-1225.
    • (2005) J. Amer. Statist. Assoc. , vol.100 , pp. 1215-1225
    • Yuan, M.1    Lin, Y.2
  • 130
    • 34548536008 scopus 로고    scopus 로고
    • On the "degrees of freedom" of the lasso
    • ZOU, H., HASTIE, T. and TIBSHIRANI, R. (2007). On the "degrees of freedom" of the lasso. Ann. Statist. 35 2173-2192.
    • (2007) Ann. Statist. , vol.35 , pp. 2173-2192
    • Zou, H.1    Hastie, T.2    Tibshirani, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.