Ad Placement Challenge

The winning solution to the Ad Placement Challenge by Criteo for the NIPS'17 Causal Inference and Machine Learning Workshop

• Report: https://arxiv.org/abs/1712.01913 (also in report/)
• Slides: https://www.slideshare.net/AlexeyGrigorev/ad-placement-challenge
• Video: https://www.youtube.com/watch?v=d5I0baV51vk

The solution is simple:

• Take the data as is, train a FTRL model (using libftrl-python)
• Post-process the predictions:
  • Apply the sigmoid fuction to the predictions and scale the result by some constant value
  • For each group add +15 to the max value

Environment:

• ubuntu 14.04
• 8 cores, 32 gb of ram
• anaconda with python 3.5.2
• additional packages not from anaconda: tqdm and ftrl

Running the solution

• mkdir tmp
• python 01-cv-split.py # takes ~22 mins
• python 02-prepare-data.py # ~17 mins
• python 03-train-model.py # ~25 mins
• python 04-predict.py # ~4.5 hours
• gzip pred_ftrl.txt
• submit pred_ftrl.txt.gz (~1.5 hours)