Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 14 Aug 2023.
Builds on
10 of 33 references.
References · 10
- 2019Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systemscited by 107
- 2019Embarrassingly Shallow Autoencoders for Sparse Datacited by 94
- 2017Latent Relational Metric Learning via Memory-based Attention for Collaborative Rankingcited by 39
- 2018Sequential Variational Autoencoders for Collaborative Filteringcited by 31
- 2021Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systemscited by 8
- 2021Beyond NDCG: behavioral testing of recommender systems with RecListcited by 8
- 2018Off-line vs. On-line Evaluation of Recommender Systems in Small E-commercecited by 6
- 2020Orthogonal Inductive Matrix Completioncited by 5
- 2015Evaluation of recommender systems in streaming environmentscited by 2
- 2023EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deploymentscited by 1
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Citing papers per year: 2025: 1.
1 paper.