Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
This addresses a critical issue for researchers and industry practitioners by providing a more reliable offline metric to bridge the gap between academic evaluation and real-world application, though it is incremental in improving existing methodology.
The paper tackled the problem of offline evaluation metrics failing to predict online performance in recommender systems, showing that penalizing popular items and considering transaction time significantly improves model selection for live systems, with results averaged over five large real-world datasets.
The evaluation of recommendation systems is a complex task. The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives. The majority of recently published papers benchmark their methods using ill-posed offline evaluation methodology that often fails to predict true online performance. Because of this, the impact that academic research has on the industry is reduced. The aim of our research is to investigate and compare the online performance of offline evaluation metrics. We show that penalizing popular items and considering the time of transactions during the evaluation significantly improves our ability to choose the best recommendation model for a live recommender system. Our results, averaged over five large-size real-world live data procured from recommenders, aim to help the academic community to understand better offline evaluation and optimization criteria that are more relevant for real applications of recommender systems.