@techreport{TD:100613,
	att_abstract={{We describe our solution for the KDD Cup 2011 track 2 challenge. Our solution relies heavily on ensembling together diverse individual models for the prediction task, and achieved a final leaderboard misclassification rate of 3.8863\%. This paper provides details on both the modeling and ensemble creation steps.}},
	att_authors={sb799t, rw218j, cs929g, am6864, yh573v, aa1327, da1287, sk2362, gm1461, sa2858},
	att_categories={},
	att_copyright={{ACM}},
	att_copyright_notice={{(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in KDD CUP 2011 workshop {{, 2011-08-21}}.
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={data mining,  machine learning,  collaborative ranking,  kdd cup},
	att_techdoc={true},
	att_techdoc_key={TD:100613},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100613_DS1_2011-08-19T01:52:22.267Z.pdf},
	author={Suhrid Balakrishnan and Rensheng Wang and Carlos Scheidegger and Angus Maclellan and Yifan Hu and Aaron Archer and David Applegate and Shankar Krishnan and Guang-qin Ma and Siu-tong Au},
	institution={{KDD CUP 2011 workshop.}},
	month={August},
	title={{Combining Predictors for Recommending Music: the False Positives' approach to KDD Cup track 2}},
	year=2011,
}