Evaluation of recommender systems in streaming environments

Understand

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.