  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.
