ICML Exploration & Exploitation Challenge: Keep it simple!

Olivier Nicol, Jérémie Mary, Philippe Preux
Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, PMLR 26:62-85, 2012.

Abstract

Recommendation has become a key feature in the economy of a lot of companies (online shopping, search engines...). There is a lot of work going on regarding recommender systems and there is still a lot to do to improve them. Indeed nowadays in many companies most of the job is done by hand. Moreover even when a supposedly smart recommender system is designed, it is hard to evaluate it without using real audience which obviously involves economic issues. The ICML Exploration & Exploitation challenge is an attempt to make people propose efficient recommendation techniques and particularly focuses on limited computational resources. The challenge also proposes a framework to address the problem of evaluating a recommendation algorithm with real data. We took part in this challenge and achieved the best performances; this paper aims at reporting on this achievement; we also discuss the evaluation process and propose a better one for future challenges of the same kind.

Cite this Paper

BibTeX
@InProceedings{pmlr-v26-nicol12a, title = {ICML Exploration & Exploitation Challenge: Keep it simple!}, author = {Nicol, Olivier and Mary, Jérémie and Preux, Philippe}, booktitle = {Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2}, pages = {62--85}, year = {2012}, editor = {Glowacka, Dorota and Dorard, Louis and Shawe-Taylor, John}, volume = {26}, series = {Proceedings of Machine Learning Research}, address = {Bellevue, Washington, USA}, month = {02 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v26/nicol12a/nicol12a.pdf}, url = {https://proceedings.mlr.press/v26/nicol12a.html}, abstract = {Recommendation has become a key feature in the economy of a lot of companies (online shopping, search engines...). There is a lot of work going on regarding recommender systems and there is still a lot to do to improve them. Indeed nowadays in many companies most of the job is done by hand. Moreover even when a supposedly smart recommender system is designed, it is hard to evaluate it without using real audience which obviously involves economic issues. The ICML Exploration & Exploitation challenge is an attempt to make people propose efficient recommendation techniques and particularly focuses on limited computational resources. The challenge also proposes a framework to address the problem of evaluating a recommendation algorithm with real data. We took part in this challenge and achieved the best performances; this paper aims at reporting on this achievement; we also discuss the evaluation process and propose a better one for future challenges of the same kind.} }
Endnote
%0 Conference Paper %T ICML Exploration & Exploitation Challenge: Keep it simple! %A Olivier Nicol %A Jérémie Mary %A Philippe Preux %B Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2 %C Proceedings of Machine Learning Research %D 2012 %E Dorota Glowacka %E Louis Dorard %E John Shawe-Taylor %F pmlr-v26-nicol12a %I PMLR %P 62--85 %U https://proceedings.mlr.press/v26/nicol12a.html %V 26 %X Recommendation has become a key feature in the economy of a lot of companies (online shopping, search engines...). There is a lot of work going on regarding recommender systems and there is still a lot to do to improve them. Indeed nowadays in many companies most of the job is done by hand. Moreover even when a supposedly smart recommender system is designed, it is hard to evaluate it without using real audience which obviously involves economic issues. The ICML Exploration & Exploitation challenge is an attempt to make people propose efficient recommendation techniques and particularly focuses on limited computational resources. The challenge also proposes a framework to address the problem of evaluating a recommendation algorithm with real data. We took part in this challenge and achieved the best performances; this paper aims at reporting on this achievement; we also discuss the evaluation process and propose a better one for future challenges of the same kind.
RIS
TY - CPAPER TI - ICML Exploration & Exploitation Challenge: Keep it simple! AU - Olivier Nicol AU - Jérémie Mary AU - Philippe Preux BT - Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2 DA - 2012/05/02 ED - Dorota Glowacka ED - Louis Dorard ED - John Shawe-Taylor ID - pmlr-v26-nicol12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 26 SP - 62 EP - 85 L1 - http://proceedings.mlr.press/v26/nicol12a/nicol12a.pdf UR - https://proceedings.mlr.press/v26/nicol12a.html AB - Recommendation has become a key feature in the economy of a lot of companies (online shopping, search engines...). There is a lot of work going on regarding recommender systems and there is still a lot to do to improve them. Indeed nowadays in many companies most of the job is done by hand. Moreover even when a supposedly smart recommender system is designed, it is hard to evaluate it without using real audience which obviously involves economic issues. The ICML Exploration & Exploitation challenge is an attempt to make people propose efficient recommendation techniques and particularly focuses on limited computational resources. The challenge also proposes a framework to address the problem of evaluating a recommendation algorithm with real data. We took part in this challenge and achieved the best performances; this paper aims at reporting on this achievement; we also discuss the evaluation process and propose a better one for future challenges of the same kind. ER -
APA
Nicol, O., Mary, J. & Preux, P.. (2012). ICML Exploration & Exploitation Challenge: Keep it simple!. Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, in Proceedings of Machine Learning Research 26:62-85 Available from https://proceedings.mlr.press/v26/nicol12a.html.

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