  Evaluation of recommender systems is typically done with finite datasets.
This means that conventional evaluation methodologies are only applicable in
offline experiments, where data and models are stationary. However, in real
world systems, user feedback is continuously generated, at unpredictable rates.
Given this setting, one important issue is how to evaluate algorithms in such a
streaming data environment. In this paper we propose a prequential evaluation
protocol for recommender systems, suitable for streaming data environments, but
also applicable in stationary settings. Using this protocol we are able to
monitor the evolution of algorithms' accuracy over time. Furthermore, we are
able to perform reliable comparative assessments of algorithms by computing
significance tests over a sliding window. We argue that besides being suitable
for streaming data, prequential evaluation allows the detection of phenomena
that would otherwise remain unnoticed in the evaluation of both offline and
online recommender systems.
