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The Implementation and Optimization of Matrix Decomposition Based Collaborative Filtering Task on X86 Platform

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Benchmarking, Measuring, and Optimizing (Bench 2019)

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Abstract

With the rapid development of the information age, the recommendation system becomes more and more significant to help people find hidden information from the big dataset in daily lives. Collaborative filtering is a popular technology often used in recommendation systems, which recommend items to users according to other users having the similar behaviors with the target user or according to the items having the alike properties with the target item. In this paper, we implement a parallel collaborative filtering algorithm called ALS-WR on the AMD x86 platform and use an adaptive granularity tuning method to obtain the best performance of 124.86 s in 30 training rounds.

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Author information

Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Tianshu Hao

  2. Google, Inc., Mountain View, CA, USA

    Ziping Zheng

Authors
  1. Tianshu Hao
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  2. Ziping Zheng
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Corresponding author

Correspondence to Ziping Zheng .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Wanling Gao

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

  3. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Geoffrey Fox

  4. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Xiaoyi Lu

  5. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA

    Dan Stanzione

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Hao, T., Zheng, Z. (2020). The Implementation and Optimization of Matrix Decomposition Based Collaborative Filtering Task on X86 Platform. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-49556-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49555-8

  • Online ISBN: 978-3-030-49556-5

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