mob-ai opened a new pull request #27000: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component URL: https://github.com/apache/spark/pull/27000 <!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### 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
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