arXiv (OAI Expanded)
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AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their algorithms based on a combination of feedback from stakeholder teams, domain expertise, and insights from data analyses. Yet the nuances of how and where recommendations perform well o
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