ICML Exploration & Exploitation challenge: Keep it simple!

Olivier Nicol 1, 2 Jérémie Mary 1, 2 Philippe Preux 1, 2
1 INRIA Lille - Nord Europe - SEQUEL
2 LIFL - Laboratoire d'Informatique Fondamentale de Lille
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.
Keywords : machine learning Recommendation Bayesian Exploration/Exploitation Evaluation Click prediction
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Conference papers
Dorota Glowacka and Louis Dorard and John Shawe-Taylor. Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Jul 2011, Bellevue, Washington, United States. sans, 26, pp.62-85, Journal of Machine Learning Research - Proceedings Track
https://hal.inria.fr/hal-00747725
Contributor : Preux Philippe <>
Submitted on : Thursday, November 8, 2012 - 3:23:06 PM
Last modification on : Friday, July 12, 2013 - 1:31:13 PM

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Olivier Nicol, Jérémie Mary, Philippe Preux. ICML Exploration & Exploitation challenge: Keep it simple!. Dorota Glowacka and Louis Dorard and John Shawe-Taylor. Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Jul 2011, Bellevue, Washington, United States. sans, 26, pp.62-85, Journal of Machine Learning Research - Proceedings Track. <hal-00747725>

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