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https://issues.apache.org/jira/browse/SPARK-29224?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17002402#comment-17002402
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Ruslan Dautkhanov commented on SPARK-29224:
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E.g. would this work with 0.1m or 1m sparse features?
> Implement Factorization Machines as a ml-pipeline component
> -----------------------------------------------------------
>
> Key: SPARK-29224
> URL: https://issues.apache.org/jira/browse/SPARK-29224
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Affects Versions: 3.0.0
> Reporter: mob-ai
> Assignee: mob-ai
> Priority: Major
> Fix For: 3.0.0
>
> Attachments: url_loss.xlsx
>
>
> 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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