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Running field-aware factorization machines on the Criteo data

Home Page: https://www.kaggle.com/c/criteo-display-ad-challenge

Python 19.15% Shell 4.23% Makefile 3.15% C++ 73.47%
field-aware-factorization-machines factorization-machines kaggle ctr-prediction hive

criteo-ffm's Introduction

FFM on the Criteo data

Try to replicate the result of Kaggle Display Advertising Challenge by using the following implementations of field-aware factorization machines (FFMs):

Data

Download and convert the full dataset into CSV format:

./data.sh
ln -s train.csv tr.csv
ln -s test.csv te.csv

Or, use tiny data:

ln -s train.tiny.csv tr.csv
ln -s test.tiny.csv te.csv

Usage

Build LIBFFM and preprocess data as the winning solution did:

make

Move to libffm/ or hivemall/ and follow the instructions.

References

criteo-ffm's People

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