Solving RecSys Challenge 2015 by Linear Models, Gradient Boosted Trees and Metric Optimization

    The RecSys Challenge 2015 task requested prediction for items purchased in online web shop sessions. We describe our method that reached fifth place on the leaderboard by constructing a large number of item, session, and session-item features and using linear models and gradient boosted trees for learning. An important element of our method included optimization for the specific evaluation metric.

    Róbert Pálovics, Péter Szalai, Levente Kocsis, Adrienn Szabó, Erzsébet Frigó, Júlia Pap, Zsófia K. Nyikes, and András A. Benczúr
    RecSys '15 Challenge