Study of a bias in the offline evaluation of a recommendation algorithm
Information Retrieval
2015-11-05 v1 Machine Learning
Machine Learning
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 describes this bias and discuss the relevance of a weighted offline evaluation to reduce this bias for different classes of recommendation algorithms.
Keywords
Cite
@article{arxiv.1511.01280,
title = {Study of a bias in the offline evaluation of a recommendation algorithm},
author = {Arnaud De Myttenaere and Boris Golden and Bénédicte Le Grand and Fabrice Rossi},
journal= {arXiv preprint arXiv:1511.01280},
year = {2015}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1407.0822