publication . Conference object . Preprint . 2015

Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark

Winlaw, Manda; Hynes, Michael B.; Caterini, Anthony; De Sterck, Hans;
Open Access
  • Published: 12 Aug 2015
  • Publisher: IEEE
Abstract
Comment: Proceedings of ICPADS 2015, Melbourne, AU. 10 pages; 6 figures; 4 tables
Subjects
free text keywords: Matrix decomposition, Acceleration, Spark (mathematics), Computational science, MovieLens, Recommender system, Collaborative filtering, Line search, Real-time computing, Computer science, Nonlinear conjugate gradient method, Mathematics - Numerical Analysis, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Information Retrieval, Computer Science - Numerical Analysis, 65K05, G.1.3, G.1.6
Related Organizations
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
34 references, page 1 of 3

[1] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 285-295. [OpenAIRE]

[2] Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008, pp. 426-434.

[3] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutie´Rrez, “Recommender systems survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.

[4] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Comput., vol. 7, no. 1, pp. 76-80, 2003.

[5] R. M. Bell and Y. Koren, “Lessons from the Netflix prize challenge,” SIGKDD Explor. Newsl., vol. 9, no. 2, pp. 75-79, 2007.

[6] C. C. Johnson, “Logistic matrix factorization for implicit feedback data,” in NIPS Workshop on Distributed Machine Learning and Matrix Computations, 2014.

[7] G. Dror, N. Koenigstein, Y. Koren, and M. Weimer, “The Yahoo! music dataset and KDD-cup '11,” JMLR: Workshop and Conference Proceedings, vol. 18, pp. 3-18, 2012.

[8] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30-37, 2009.

[9] S. Funk, “Netflix update: Try this at home,” http://sifter.org/ simon/journal/20061211.html, 2006.

[10] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 263-272. [OpenAIRE]

[11] J. Nocedal and S. J. Wright, Numerical Optimization, 2nd ed. New York: Springer, 2006.

[12] H. De Sterck and M. Winlaw, “A nonlinearly preconditioned conjugate gradient algorithm for rank-R canonical tensor approximation,” Numer. Linear Algebra Appl., vol. 22, pp. 410-432, 2015.

[13] P. Brune, M. G. Knepley, B. F. Smith, and X. Tu, “Composing scalable nonlinear algebraic solvers,” SIAM Review, forthcoming.

[14] H. De Sterck, “A nonlinear GMRES optimization algorithm for canonical tensor decomposition,” SIAM J. Sci. Comput., vol. 34, pp. A1351- A1379, 2012.

[15] H. Fang and Y. Saad, “Two classes of multisecant methods for nonlinear acceleration,” Numer. Linear Algebra Appl., vol. 16, pp. 197-221, 2009.

34 references, page 1 of 3
Abstract
Comment: Proceedings of ICPADS 2015, Melbourne, AU. 10 pages; 6 figures; 4 tables
Subjects
free text keywords: Matrix decomposition, Acceleration, Spark (mathematics), Computational science, MovieLens, Recommender system, Collaborative filtering, Line search, Real-time computing, Computer science, Nonlinear conjugate gradient method, Mathematics - Numerical Analysis, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Information Retrieval, Computer Science - Numerical Analysis, 65K05, G.1.3, G.1.6
Related Organizations
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
34 references, page 1 of 3

[1] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th International Conference on World Wide Web, 2001, pp. 285-295. [OpenAIRE]

[2] Y. Koren, “Factorization meets the neighborhood: a multifaceted collaborative filtering model,” in 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008, pp. 426-434.

[3] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutie´Rrez, “Recommender systems survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.

[4] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Comput., vol. 7, no. 1, pp. 76-80, 2003.

[5] R. M. Bell and Y. Koren, “Lessons from the Netflix prize challenge,” SIGKDD Explor. Newsl., vol. 9, no. 2, pp. 75-79, 2007.

[6] C. C. Johnson, “Logistic matrix factorization for implicit feedback data,” in NIPS Workshop on Distributed Machine Learning and Matrix Computations, 2014.

[7] G. Dror, N. Koenigstein, Y. Koren, and M. Weimer, “The Yahoo! music dataset and KDD-cup '11,” JMLR: Workshop and Conference Proceedings, vol. 18, pp. 3-18, 2012.

[8] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30-37, 2009.

[9] S. Funk, “Netflix update: Try this at home,” http://sifter.org/ simon/journal/20061211.html, 2006.

[10] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 263-272. [OpenAIRE]

[11] J. Nocedal and S. J. Wright, Numerical Optimization, 2nd ed. New York: Springer, 2006.

[12] H. De Sterck and M. Winlaw, “A nonlinearly preconditioned conjugate gradient algorithm for rank-R canonical tensor approximation,” Numer. Linear Algebra Appl., vol. 22, pp. 410-432, 2015.

[13] P. Brune, M. G. Knepley, B. F. Smith, and X. Tu, “Composing scalable nonlinear algebraic solvers,” SIAM Review, forthcoming.

[14] H. De Sterck, “A nonlinear GMRES optimization algorithm for canonical tensor decomposition,” SIAM J. Sci. Comput., vol. 34, pp. A1351- A1379, 2012.

[15] H. Fang and Y. Saad, “Two classes of multisecant methods for nonlinear acceleration,” Numer. Linear Algebra Appl., vol. 16, pp. 197-221, 2009.

34 references, page 1 of 3
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publication . Conference object . Preprint . 2015

Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark

Winlaw, Manda; Hynes, Michael B.; Caterini, Anthony; De Sterck, Hans;