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

• URL: http://arxiv.org/abs/2507.09566v1
• Date: Sun, 13 Jul 2025 10:24:41 GMT
• Title: Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems
• Authors: Timo Wilm, Philipp Normann,
• Abstract summary: We introduce a pragmatic strategy for identifying offline metrics that align with online impact.<n>We validate the strategy through a large-scale online experiment in the field of session-based recommender systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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