Combining Predictors for Recommending Music:the False Positives’ approach to KDD Cup track 2

Suhrid Balakrishnan, Rensheng Wang, Carlos Scheidegger, Angus MacLellan, Yifan Hu, Aaron Archer, Shankar Krishnan, David Applegate, Guang Qin Ma, S. Tom Au
Proceedings of KDD Cup 2011, PMLR 18:199-213, 2012.

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/Test 1 misclassification rate of 3.8863%. This paper provides details on both the modeling and ensemble creation steps.

Cite this Paper

BibTeX
@InProceedings{pmlr-v18-balakrishnan12a, title = {Combining Predictors for Recommending Music:the False Positives’ approach to KDD Cup track 2}, author = {Balakrishnan, Suhrid and Wang, Rensheng and Scheidegger, Carlos and MacLellan, Angus and Hu, Yifan and Archer, Aaron and Krishnan, Shankar and Applegate, David and Ma, Guang Qin and Au, S. Tom}, booktitle = {Proceedings of KDD Cup 2011}, pages = {199--213}, year = {2012}, editor = {Dror, Gideon and Koren, Yehuda and Weimer, Markus}, volume = {18}, series = {Proceedings of Machine Learning Research}, month = {21 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v18/balakrishnan12a/balakrishnan12a.pdf}, url = {https://proceedings.mlr.press/v18/balakrishnan12a.html}, 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/Test 1 misclassification rate of 3.8863%. This paper provides details on both the modeling and ensemble creation steps.} }
Endnote
%0 Conference Paper %T Combining Predictors for Recommending Music:the False Positives’ approach to KDD Cup track 2 %A Suhrid Balakrishnan %A Rensheng Wang %A Carlos Scheidegger %A Angus MacLellan %A Yifan Hu %A Aaron Archer %A Shankar Krishnan %A David Applegate %A Guang Qin Ma %A S. Tom Au %B Proceedings of KDD Cup 2011 %C Proceedings of Machine Learning Research %D 2012 %E Gideon Dror %E Yehuda Koren %E Markus Weimer %F pmlr-v18-balakrishnan12a %I PMLR %P 199--213 %U https://proceedings.mlr.press/v18/balakrishnan12a.html %V 18 %X 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/Test 1 misclassification rate of 3.8863%. This paper provides details on both the modeling and ensemble creation steps.
RIS
TY - CPAPER TI - Combining Predictors for Recommending Music:the False Positives’ approach to KDD Cup track 2 AU - Suhrid Balakrishnan AU - Rensheng Wang AU - Carlos Scheidegger AU - Angus MacLellan AU - Yifan Hu AU - Aaron Archer AU - Shankar Krishnan AU - David Applegate AU - Guang Qin Ma AU - S. Tom Au BT - Proceedings of KDD Cup 2011 DA - 2012/06/01 ED - Gideon Dror ED - Yehuda Koren ED - Markus Weimer ID - pmlr-v18-balakrishnan12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 18 SP - 199 EP - 213 L1 - http://proceedings.mlr.press/v18/balakrishnan12a/balakrishnan12a.pdf UR - https://proceedings.mlr.press/v18/balakrishnan12a.html AB - 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/Test 1 misclassification rate of 3.8863%. This paper provides details on both the modeling and ensemble creation steps. ER -
APA
Balakrishnan, S., Wang, R., Scheidegger, C., MacLellan, A., Hu, Y., Archer, A., Krishnan, S., Applegate, D., Ma, G.Q. & Au, S.T.. (2012). Combining Predictors for Recommending Music:the False Positives’ approach to KDD Cup track 2. Proceedings of KDD Cup 2011, in Proceedings of Machine Learning Research 18:199-213 Available from https://proceedings.mlr.press/v18/balakrishnan12a.html.

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