Outbrain pairs relevant content with curious readers in about 250 billion personalized recommendations every month across many thousands of sites. In this competition, we predict which pieces of content its global base of users are likely to click on. Improving Outbrain’s recommendation algorithm will mean more users uncover stories that satisfy their individual tastes.
To view full report including problem definiation, data preparation, models and evaluation metrics, and deploymnet, please navigate to this file: "outbrain-click-prediction_report.pdf"
To view models source code including Logistic Regression, Factorization Machines, Random forest, and Multilayer Perceptron, please naviate to this folder: "src"