Context-based Recommendations using Deep Learning

Project by Jonas

Abstract

Deliver an E2E CTR (Click through rates) Prediction Model using Deep Learning techniques to improve Outbrain existing Recommendations. The Project involves building a working pipeline with Classic Machine Learning techniques, to improve it with DL using Tensorflow/Keras and to evaluate the models with Outbrain Metrics.

 

Challenges

  • Working with large amount of correlated data
  • Setting up and running a Pipeline on a remote GPU
  • Testing and fine-tuning state of the art optimization methods

 

Achievements (according to KPIs):

  • Improved the model in production by 30% in RMSE
  • Delivered a working pipeline
  • Found useful insights on how DL methods behave on Outbrain Data

 

Further development

  • Improve the model performances in terms of prediction time
  • Write the model in production code
  • A/B test the model performances online

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