PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

ICML Exploration & Exploitation challenge: Keep it simple!
olivier nicol, Jérémie Mary and Philippe Preux
Journal of Machine Learning research Workshop and Conference Proceedings Volume 26, pp. 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9618
Deposited By:Philippe Preux
Deposited On:01 December 2012