ycjuan/kaggle-2014-criteo

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1254 stars · 607 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

The winning '3 Idiots' solution code for the 2014 Kaggle Criteo Display Advertising Challenge, built around field-aware factorization machines (FFM). It includes C++ executables and Python scripts for converting, preparing, and running predictions on the Criteo dataset.

Use cases

• reproduce the winning solution for the Kaggle Criteo display advertising challenge
• train field-aware factorization machine models on CTR prediction data
• convert the released Criteo text dataset to competition csv format
• learn how top Kaggle teams approached large-scale click-through-rate prediction
• run FFM-based experiments on large advertising datasets

When to choose

• you want to replicate or study the winning 2014 Criteo competition approach
• you need a proven FFM pipeline for click-through-rate prediction
• you have the required 40GB+ memory Unix environment and the Criteo dataset

When to avoid

• you need a modern, maintained CTR prediction library with active support
• you work on Windows or lack 40GB+ RAM and 100GB disk space
• you want a general-purpose machine learning framework rather than a competition-specific solution

Facets

library · maturity maintenance

machine-learning data-science etl machine-learning data-science cpp python kaggle criteo factorization-machines ffm click-through-rate display-advertising competition-solution algorithms linux

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