Committee Based Prediction System for Recommendation: KDD Cup 2011, Track2

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H. Zhang, E. Riedl, V. Petrushin, S. Pal, J. Spoelstra ;
Proceedings of KDD Cup 2011, PMLR 18:215-229, 2012.

Abstract

This paper describes a solution to the 2011 KDD Cup competition, Track2: discriminating between highly rated tracks and unrated tracks in a Yahoo! Music dataset. Our approach was to use supervised learning based on 65 features generated using various techniques such as collaborative filtering, SVD, and similarity scoring. During our modeling stage, we created a number of predictors including logistic regression, artificial neural networks and gradient-boosted decision trees. To further improve robustness and reduce the variance, we used three of our top performing models and took a weighted average for the final submission, which achieved 4.3768% error.

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