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  1. Spark
  2. SPARK-29224

Implement Factorization Machines as a ml-pipeline component

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Details

    • New Feature
    • Status: Resolved
    • Major
    • Resolution: Fixed
    • 3.0.0
    • 3.0.0
    • ML
    • None

    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

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