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

Authors:
Dmitry Efimov;Lucas Silva;Benjamin Solecki
Affiliations:
Moscow State University, Moscow, Russia;PWH, Belo Horizonte, Brazil;Pasadena CA
Venue:
Proceedings of the 2013 KDD Cup 2013 Workshop
Year:
2013

Citing 4
Cited 0

Term-weighting approaches in automatic text retrieval

Information Processing and Management: an International Journal
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An efficient boosting algorithm for combining preferences

The Journal of Machine Learning Research
Linear Mixed-Effects Models Using R: A Step-by-Step Approach

Linear Mixed-Effects Models Using R: A Step-by-Step Approach

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.