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arXiv · 2111.09963

Beyond NDCG: behavioral testing of recommender systems with RecList

Patrick John Chia·Jacopo Tagliabue·Federico Bianchi·Chloe He·Brian Ko
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Abstract

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points. However, real-world behavior is undoubtedly nuanced: ad hoc error analysis and deployment-specific tests must be employed to ensure the desired quality in actual deployments. In this paper, we propose RecList, a behavioral-based testing methodology. RecList organizes recommender systems by use case and introduces a general plug-and-play procedure to scale up behavioral testing. We demonstrate its capabilities by analyzing known algorithms and black-box commercial systems, and we release RecList as an open source, extensible package for the community.

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Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Chloe He, Brian Ko. 2022-03-27. Beyond NDCG: behavioral testing of recommender systems with RecList. https://doi.org/10.1145/3487553.3524215

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