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  1. Spark
  2. SPARK-29224

Implement Factorization Machines as a ml-pipeline component

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    Details

    • Type: New Feature
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 3.0.0
    • Fix Version/s: 3.0.0
    • Component/s: ML
    • Labels:
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      Description

      Factorization Machines is widely used in advertising and recommendation system to estimate CTR(click-through rate).
      Advertising and recommendation system usually has a lot of data, so we need Spark to estimate the CTR, and Factorization Machines are common ml model to estimate CTR.

      Goal: Implement Factorization Machines as a ml-pipeline component

      Requirements:
      1. loss function supports: logloss, mse
      2. optimizer: mini batch SGD

      References:
      1. S. Rendle, “Factorization machines,” in Proceedings of IEEE International Conference on Data Mining (ICDM), pp. 995–1000, 2010.
      https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf

        Attachments

        1. url_loss.xlsx
          17 kB
          mob-ai

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            • Assignee:
              mob-ai mob-ai
              Reporter:
              mob-ai mob-ai
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              Dates

              • Created:
                Updated:
                Resolved: