Reducing offline evaluation bias of collaborative filtering algorithms
Abstract
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.
Cite
@article{arxiv.1506.04135,
title = {Reducing offline evaluation bias of collaborative filtering algorithms},
author = {Arnaud De Myttenaere and Boris Golden and Bénédicte Le Grand and Fabrice Rossi},
journal= {arXiv preprint arXiv:1506.04135},
year = {2015}
}
Comments
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)