Algorithmic acceleration of parallel ALS for collaborative filtering: speeding up distributed big data recommendation in spark

Manda Winlaw, Michael Berkeley Hynes, Anthony Caterini, Hans De Sterck

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

13 Citations (Scopus)
Original languageEnglish
Title of host publicationProceedings of 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS 2015)
Subtitle of host publication14-17 December 2015, Melbourne, Victoria, Australia
EditorsTao Gu, Vikto K. Prasanna
Place of PublicationPiscataway, NJ
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages682-691
Number of pages10
ISBN (Print)9780769557854
DOIs
Publication statusPublished - 2015
EventInternational Conference on Parallel and Distributed Systems 2015 - Melbourne, Australia
Duration: 14 Dec 201517 Dec 2015
Conference number: 21st
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7381113 (IEEE Conference Proceedings)

Conference

ConferenceInternational Conference on Parallel and Distributed Systems 2015
Abbreviated titleICPADS 2015
CountryAustralia
CityMelbourne
Period14/12/1517/12/15
Internet address

Keywords

  • Recommendation systems
  • collaborative filtering
  • parallel optimization algorithms
  • matrix factorization
  • Apache Spark
  • Big Data
  • scalable methods

Cite this

Winlaw, M., Hynes, M. B., Caterini, A., & De Sterck, H. (2015). Algorithmic acceleration of parallel ALS for collaborative filtering: speeding up distributed big data recommendation in spark. In T. Gu, & V. K. Prasanna (Eds.), Proceedings of 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS 2015): 14-17 December 2015, Melbourne, Victoria, Australia (pp. 682-691). Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICPADS.2015.91