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

Róbert Pálovics,Peter Szalai,Levente Kocsis,Adrienn Szabó,Erzsébet Frigó,Júlia Pap,Zsófia K. Nyikes,András A. Benczúr

Proceedings of the 2015 International ACM Recommender Systems Challenge（2015）

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摘要

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.

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关键词

gradient boosting,neural networks,e commerce,classification

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