Context-based Recommendations using Deep Learning

Project by Jonas


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.



  • 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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