KDD Cup 2013 - author-paper identification challenge: second place team

This paper describes our submission to the KDD Cup 2013 Track 1 Challenge: Author-Paper Indentification in the Microsoft Academic Search database. Our approach is based on Gradient Boosting Machine (GBM) of Friedman ([5]) and deep feature engineering. The method was second in the final standings with Mean Average Precision (MAP) of 0.98144, while the winning submission scored 0.98259.

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