[SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component #27000
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@srowen I opened a new PR to resolve pyspark unittests failure. I used following command to run pyspark tests. And I fixed FM python doc error. |
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Test build #4974 has finished for PR 27000 at commit
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Mar 30, 2020
…omponent ### What changes were proposed in this pull request? Implement Factorization Machines as a ml-pipeline component 1. loss function supports: logloss, mse 2. optimizer: GD, adamW ### Why are the changes needed? 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. 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 ### Does this PR introduce any user-facing change? No ### How was this patch tested? run unit tests Closes apache#27000 from mob-ai/ml/fm. Authored-by: zhanjf <zhanjf@mob.com> Signed-off-by: Sean Owen <srowen@gmail.com>
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What changes were proposed in this pull request?
Implement Factorization Machines as a ml-pipeline component
Why are the changes needed?
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.
References:
https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
Does this PR introduce any user-facing change?
No
How was this patch tested?
run unit tests