Collaborative Filtering Ensemble

Michael Jahrer, Andreas Töscher
Proceedings of KDD Cup 2011, PMLR 18:61-74, 2012.

Abstract

This paper provides the solution of the team “commendo” on the Track1 dataset of the KDD Cup 2011 Dror et al.. Yahoo Labs provides a snapshot of their music-rating database as dataset for the competition. We get approximately 260 million ratings from 1 million users on 600k items. Timestamp and taxonomy information are added to the ratings. The goal of the competition was to predict unknown ratings on a testset with RMSE as error measure. Our final submission is a blend of different collaborative filtering algorithms. The algorithms are trained consecutively and they are blended together with a neural network.

Cite this Paper


BibTeX
@InProceedings{pmlr-v18-jahrer12a, title = {Collaborative Filtering Ensemble}, author = {Jahrer, Michael and Töscher, Andreas}, booktitle = {Proceedings of KDD Cup 2011}, pages = {61--74}, 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/jahrer12a/jahrer12a.pdf}, url = {https://proceedings.mlr.press/v18/jahrer12a.html}, abstract = {This paper provides the solution of the team “commendo” on the Track1 dataset of the KDD Cup 2011 Dror et al.. Yahoo Labs provides a snapshot of their music-rating database as dataset for the competition. We get approximately 260 million ratings from 1 million users on 600k items. Timestamp and taxonomy information are added to the ratings. The goal of the competition was to predict unknown ratings on a testset with RMSE as error measure. Our final submission is a blend of different collaborative filtering algorithms. The algorithms are trained consecutively and they are blended together with a neural network.} }
Endnote
%0 Conference Paper %T Collaborative Filtering Ensemble %A Michael Jahrer %A Andreas Töscher %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-jahrer12a %I PMLR %P 61--74 %U https://proceedings.mlr.press/v18/jahrer12a.html %V 18 %X This paper provides the solution of the team “commendo” on the Track1 dataset of the KDD Cup 2011 Dror et al.. Yahoo Labs provides a snapshot of their music-rating database as dataset for the competition. We get approximately 260 million ratings from 1 million users on 600k items. Timestamp and taxonomy information are added to the ratings. The goal of the competition was to predict unknown ratings on a testset with RMSE as error measure. Our final submission is a blend of different collaborative filtering algorithms. The algorithms are trained consecutively and they are blended together with a neural network.
RIS
TY - CPAPER TI - Collaborative Filtering Ensemble AU - Michael Jahrer AU - Andreas Töscher BT - Proceedings of KDD Cup 2011 DA - 2012/06/01 ED - Gideon Dror ED - Yehuda Koren ED - Markus Weimer ID - pmlr-v18-jahrer12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 18 SP - 61 EP - 74 L1 - http://proceedings.mlr.press/v18/jahrer12a/jahrer12a.pdf UR - https://proceedings.mlr.press/v18/jahrer12a.html AB - This paper provides the solution of the team “commendo” on the Track1 dataset of the KDD Cup 2011 Dror et al.. Yahoo Labs provides a snapshot of their music-rating database as dataset for the competition. We get approximately 260 million ratings from 1 million users on 600k items. Timestamp and taxonomy information are added to the ratings. The goal of the competition was to predict unknown ratings on a testset with RMSE as error measure. Our final submission is a blend of different collaborative filtering algorithms. The algorithms are trained consecutively and they are blended together with a neural network. ER -
APA
Jahrer, M. & Töscher, A.. (2012). Collaborative Filtering Ensemble. Proceedings of KDD Cup 2011, in Proceedings of Machine Learning Research 18:61-74 Available from https://proceedings.mlr.press/v18/jahrer12a.html.

Related Material