Evaluation of recommender systems in streaming environments
This addresses the challenge of evaluating recommender systems in real-world, non-stationary settings for researchers and practitioners, though it is incremental as it adapts existing prequential methods to this domain.
The paper tackles the problem of evaluating recommender systems in streaming environments where user feedback is continuous and unpredictable, proposing a prequential evaluation protocol that monitors algorithm accuracy over time and enables reliable comparative assessments with significance tests.
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.