skip to main content
10.1145/2695664.2695850acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Accelerating recommender systems using GPUs

Published: 13 April 2015 Publication History

Abstract

We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi-core versions of the same algorithms. Results on the GPU are better than the results of the multi-core versions (maximum speedup of 14.8).

References

[1]
H. Andrews and C. Patterson. Singular value decompositions and digital image processing. Acoustics, Speech and Signal Processing, IEEE Transactions on, 24(1):26--53, Feb 1976.
[2]
E.-A. Baatarjav, S. Phithakkitnukoon, and R. Dantu. Group recommendation system for facebook. In Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems, OTM '08, pages 211--219, Berlin, Heidelberg, 2008. Springer-Verlag.
[3]
O. Bretscher. Linear Algebra With Applications. Pearson Education, Boston, 2013.
[4]
R. Burke. The adaptive web. In P. Brusilovsky, A. Kobsa, and W. Nejdl, editors, Lecture Notes In Computer Science, Vol. 4321., chapter Hybrid Web Recommender Systems, pages 377--408. Springer-Verlag, Berlin, Heidelberg, 2007.
[5]
R. Chandra. Parallel Programming in OpenMP. High performance computing. Morgan Kaufmann, 2001.
[6]
J. Fang, A. L. Varbanescu, and H. Sips. A comprehensive performance comparison of cuda and opencl. In Proceedings of the 2011 International Conference on Parallel Processing, ICPP '11, pages 216--225, Washington, DC, USA, 2011. IEEE Computer Society.
[7]
J. He. A Social Network-based Recommender System. PhD thesis, UCLA, Los Angeles, CA, USA, 2010. AAI3437557.
[8]
R. Hochberg. Matrix multiplication with cuda-a basic introduction to the cuda programming model. Shodor, 2012.
[9]
C.-J. Hsieh and I. S. Dhillon. Fast coordinate descent methods with variable selection for non-negative matrix factorization. In Proceedings of the 17th ACM SIGKDD, KDD '11, pages 1064--1072, New York, NY, USA, 2011. ACM.
[10]
Y. Koren and R. Bell. Advances in collaborative filtering. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 145--186. Springer US, 2011.
[11]
A. Krishnamoorthy and D. Menon. Matrix inversion using cholesky decomposition. In Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013, pages 70--72, Sept 2013.
[12]
T. Mahmood and F. Ricci. Improving recommender systems with adaptive conversational strategies. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT '09, pages 73--82, New York, NY, USA, 2009. ACM.
[13]
C. D. Meyer, editor. Matrix Analysis and Applied Linear Algebra. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, 2000.
[14]
P. Resnick and H. R. Varian. Recommender systems. Commun. ACM, 40(3):56--58, Mar. 1997.
[15]
J. Sanders and E. Kandrot. CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional, 1st edition, 2010.
[16]
B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system -- a case study. In IN ACM WEBKDD WORKSHOP, 2000.
[17]
G. Takács and D. Tikk. Alternating least squares for personalized ranking. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys '12, pages 83--90, New York, NY, USA, 2012. ACM.
[18]
N. Wilt. The CUDA Handbook: A Comprehensive Guide to GPU Programming. Pearson Education, 2013.
[19]
H.-F. Yu, C.-J. Hsieh, S. Si, and I. Dhillon. Parallel matrix factorization for recommender systems. Knowledge and Information Systems, pages 1--27, 2013.
[20]
D. Zachariah, M. Sundin, M. Jansson, and S. Chatterjee. Alternating least-squares for low-rank matrix reconstruction. Signal Processing Letters, IEEE, 19(4):231--234, April 2012.
[21]
G. Zhanchun and L. Yuying. Improving the collaborative filtering recommender system by using gpu. In Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2012 International Conference on, pages 330--333, Oct 2012.
[22]
Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel collaborative filtering for the netix prize. In Proc. 4th Int'l Conf. Algorithmic Aspects in Information and Management, LNCS 5034, pages 337--348. Springer, 2008.
[23]
M. A. Zinkevich, A. Smola, M. Weimer, and L. Li. Parallelized stochastic gradient descent. In Advances in Neural Information Processing Systems 23, pages 2595--2603, 2010.

Cited By

View all
  • (2022)A novel quantum recommender systemPhysica Scripta10.1088/1402-4896/aca4a898:1(010001)Online publication date: 19-Dec-2022
  • (2021)BALS: Blocked Alternating Least Squares for Parallel Sparse Matrix Factorization on GPUsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.306494232:9(2291-2302)Online publication date: 1-Sep-2021
  • (2020)An elastic net regularized matrix factorization technique for recommender systemsProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373847(2184-2192)Online publication date: 30-Mar-2020
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. NVIDIA CUDA
  2. parallel systems
  3. recommender systems

Qualifiers

  • Research-article

Conference

SAC 2015
Sponsor:
SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

Acceptance Rates

SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25
The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)1
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2022)A novel quantum recommender systemPhysica Scripta10.1088/1402-4896/aca4a898:1(010001)Online publication date: 19-Dec-2022
  • (2021)BALS: Blocked Alternating Least Squares for Parallel Sparse Matrix Factorization on GPUsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.306494232:9(2291-2302)Online publication date: 1-Sep-2021
  • (2020)An elastic net regularized matrix factorization technique for recommender systemsProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3373847(2184-2192)Online publication date: 30-Mar-2020
  • (2018)clMF : A fine-grained and portable alternating least squares algorithm for parallel matrix factorizationFuture Generation Computer Systems10.1016/j.future.2018.04.071Online publication date: May-2018
  • (2017)High Performance Coordinate Descent Matrix Factorization for Recommender SystemsProceedings of the Computing Frontiers Conference10.1145/3075564.3077625(117-126)Online publication date: 15-May-2017
  • (2017)Parallel CCD++ on GPU for Matrix FactorizationProceedings of the General Purpose GPUs10.1145/3038228.3038240(73-83)Online publication date: 4-Feb-2017
  • (2017)Efficient and Portable ALS Matrix Factorization for Recommender Systems2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2017.91(409-418)Online publication date: May-2017
  • (2017)A GPU-oriented online recommendation algorithm for efficient processing of time-varying continuous data streamsKnowledge and Information Systems10.1007/s10115-016-0967-353:3(637-670)Online publication date: 1-Dec-2017

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media