[jira] [Created] (SPARK-10870) Criteo Display Advertising Challenge dataset

["Peter Rudenko (JIRA)" <ji...@apache.org>]

Peter Rudenko created SPARK-10870:
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             Summary: Criteo Display Advertising Challenge dataset
                 Key: SPARK-10870
                 URL: https://issues.apache.org/jira/browse/SPARK-10870
             Project: Spark
          Issue Type: Sub-task
            Reporter: Peter Rudenko


Very useful dataset to test pipeline because of:
# "Big data" dataset - original Kaggle competition dataset is 12 gb, but there's [1tb|http://labs.criteo.com/downloads/download-terabyte-click-logs/] dataset of the same schema as well.
# Sparse models - categorical features has high cardinality
# Reproducible results - because it's public and many other distributed machine learning libraries (e.g. [wormwhole|https://github.com/dmlc/wormhole/blob/master/doc/tutorial/criteo_kaggle.rst], [parameter server|https://github.com/dmlc/parameter_server/blob/master/example/linear/criteo/README.md] etc.) have made a base line benchmarks on which we could compare.


I have some base line results with custom models (GBDT encoders and tuned LR) on spark-1.4. Will make pipelines using public spark model. [Winning solution|http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf] used GBDT encoder (not available in spark, but not difficult to make one from GBT from mllib) + hashing + factorization machine (planned for spark-1.6).



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