ICML Exploration & Exploitation challenge: Keep it simple!

Olivier Nicol 1, 2 Jérémie Mary 1, 2 Philippe Preux 1, 2
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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
Type de document :
Communication dans un congrès
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, 2012, Journal of Machine Learning Research - Proceedings Track
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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, 2012, Journal of Machine Learning Research - Proceedings Track. 〈hal-00747725〉

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