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arXiv · 2507.09566

Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems

Timo Wilm·Philipp Normann
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Abstract

A critical challenge in recommender systems is to establish reliable relationships between offline and online metrics that predict real-world performance. Motivated by recent advances in Pareto front approximation, we introduce a pragmatic strategy for identifying offline metrics that align with online impact. A key advantage of this approach is its ability to simultaneously serve multiple test groups, each with distinct offline performance metrics, in an online experiment controlled by a single model. The method is model-agnostic for systems with a neural network backbone, enabling broad applicability across architectures and domains. We validate the strategy through a large-scale online experiment in the field of session-based recommender systems on the OTTO e-commerce platform. The online experiment identifies significant alignments between offline metrics and real-word click-through rate, post-click conversion rate and units sold. Our strategy provides industry practitioners with a valuable tool for understanding offline-to-online metric relationships and making informed, data-driven decisions.

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Timo Wilm, Philipp Normann. 2025-07-13. Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems. https://doi.org/10.1145/3705328.3748111

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