Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation

Todd G. McKenzie, Chun-Sung Ferng, Yao-Nan Chen, Chun-Liang Li, Cheng-Hao Tsai, Kuan-Wei Wu, Ya-Hsuan Chang, Chung-Yi Li, Wei-Shih Lin, Shu-Hao Yu, Chieh-Yen Lin, Po-Wei Wang, Chia-Mau Ni, Wei-Lun Su, Tsung-Ting Kuo, Chen-Tse Tsai, Po-Lung Chen, Rong-Bing Chiu, Ku-Chun Chou, Yu-Cheng Chou, Chien-Chih Wang, Chen-Hung Wu, Hsuan-Tien Lin, Chih-Jen Lin, Shou-De Lin
Proceedings of KDD Cup 2011, PMLR 18:101-135, 2012.

Abstract

The Track 2 problem in KDD-Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history, but also the taxonomic information of track, artist, album, and genre. This paper describes the solution of the National Taiwan University team which ranked first place in the competition. We exploited a diverse of models (neighborhood models, latent models, Bayesian Personalized Ranking models, and random-walk models) with local blending and global ensemble to achieve 97.45% in accuracy on the testing dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v18-mckenzie12a, title = {Novel Models and Ensemble Techniques toDiscriminate Favorite Items from Unrated Onesfor Personalized Music Recommendation}, author = {McKenzie, Todd G. and Ferng, Chun-Sung and Chen, Yao-Nan and Li, Chun-Liang and Tsai, Cheng-Hao and Wu, Kuan-Wei and Chang, Ya-Hsuan and Li, Chung-Yi and Lin, Wei-Shih and Yu, Shu-Hao and Lin, Chieh-Yen and Wang, Po-Wei and Ni, Chia-Mau and Su, Wei-Lun and Kuo, Tsung-Ting and Tsai, Chen-Tse and Chen, Po-Lung and Chiu, Rong-Bing and Chou, Ku-Chun and Chou, Yu-Cheng and Wang, Chien-Chih and Wu, Chen-Hung and Lin, Hsuan-Tien and Lin, Chih-Jen and Lin, Shou-De}, booktitle = {Proceedings of KDD Cup 2011}, pages = {101--135}, 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/mckenzie12a/mckenzie12a.pdf}, url = {https://proceedings.mlr.press/v18/mckenzie12a.html}, abstract = {The Track 2 problem in KDD-Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history, but also the taxonomic information of track, artist, album, and genre. This paper describes the solution of the National Taiwan University team which ranked first place in the competition. We exploited a diverse of models (neighborhood models, latent models, Bayesian Personalized Ranking models, and random-walk models) with local blending and global ensemble to achieve 97.45% in accuracy on the testing dataset.} }
Endnote
%0 Conference Paper %T Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation %A Todd G. McKenzie %A Chun-Sung Ferng %A Yao-Nan Chen %A Chun-Liang Li %A Cheng-Hao Tsai %A Kuan-Wei Wu %A Ya-Hsuan Chang %A Chung-Yi Li %A Wei-Shih Lin %A Shu-Hao Yu %A Chieh-Yen Lin %A Po-Wei Wang %A Chia-Mau Ni %A Wei-Lun Su %A Tsung-Ting Kuo %A Chen-Tse Tsai %A Po-Lung Chen %A Rong-Bing Chiu %A Ku-Chun Chou %A Yu-Cheng Chou %A Chien-Chih Wang %A Chen-Hung Wu %A Hsuan-Tien Lin %A Chih-Jen Lin %A Shou-De Lin %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-mckenzie12a %I PMLR %P 101--135 %U https://proceedings.mlr.press/v18/mckenzie12a.html %V 18 %X The Track 2 problem in KDD-Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history, but also the taxonomic information of track, artist, album, and genre. This paper describes the solution of the National Taiwan University team which ranked first place in the competition. We exploited a diverse of models (neighborhood models, latent models, Bayesian Personalized Ranking models, and random-walk models) with local blending and global ensemble to achieve 97.45% in accuracy on the testing dataset.
RIS
TY - CPAPER TI - Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation AU - Todd G. McKenzie AU - Chun-Sung Ferng AU - Yao-Nan Chen AU - Chun-Liang Li AU - Cheng-Hao Tsai AU - Kuan-Wei Wu AU - Ya-Hsuan Chang AU - Chung-Yi Li AU - Wei-Shih Lin AU - Shu-Hao Yu AU - Chieh-Yen Lin AU - Po-Wei Wang AU - Chia-Mau Ni AU - Wei-Lun Su AU - Tsung-Ting Kuo AU - Chen-Tse Tsai AU - Po-Lung Chen AU - Rong-Bing Chiu AU - Ku-Chun Chou AU - Yu-Cheng Chou AU - Chien-Chih Wang AU - Chen-Hung Wu AU - Hsuan-Tien Lin AU - Chih-Jen Lin AU - Shou-De Lin BT - Proceedings of KDD Cup 2011 DA - 2012/06/01 ED - Gideon Dror ED - Yehuda Koren ED - Markus Weimer ID - pmlr-v18-mckenzie12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 18 SP - 101 EP - 135 L1 - http://proceedings.mlr.press/v18/mckenzie12a/mckenzie12a.pdf UR - https://proceedings.mlr.press/v18/mckenzie12a.html AB - The Track 2 problem in KDD-Cup 2011 (music recommendation) is to discriminate between music tracks highly rated by a given user from those which are overall highly rated, but not rated by the given user. The training dataset consists of not only user rating history, but also the taxonomic information of track, artist, album, and genre. This paper describes the solution of the National Taiwan University team which ranked first place in the competition. We exploited a diverse of models (neighborhood models, latent models, Bayesian Personalized Ranking models, and random-walk models) with local blending and global ensemble to achieve 97.45% in accuracy on the testing dataset. ER -
APA
McKenzie, T.G., Ferng, C., Chen, Y., Li, C., Tsai, C., Wu, K., Chang, Y., Li, C., Lin, W., Yu, S., Lin, C., Wang, P., Ni, C., Su, W., Kuo, T., Tsai, C., Chen, P., Chiu, R., Chou, K., Chou, Y., Wang, C., Wu, C., Lin, H., Lin, C. & Lin, S.. (2012). Novel Models and Ensemble Techniques to Discriminate Favorite Items from Unrated Ones for Personalized Music Recommendation. Proceedings of KDD Cup 2011, in Proceedings of Machine Learning Research 18:101-135 Available from https://proceedings.mlr.press/v18/mckenzie12a.html.

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