Decision-Oriented Recommendation Reranking: An Empirical Study of Its Promise and Limitations

Abstract

Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important trade-off between recommendation quality and serving efficiency. We investigate whether decision-oriented systems provide a useful alternative when the reranking task is fundamentally a structured choice among predefined candidate items. Specifically, we conduct a controlled empirical study of two decision-oriented systems, TypeSafe Jev and OpenAI Decisions, for recommendation reranking. Across three Amazon Reviews domains and four candidate-set sizes, we compare these systems against recommendation-specific models and pointwise and listwise LLM-based rerankers, systematically evaluating recommendation effectiveness, candidate-order robustness, and efficiency and scalability. Our results reveal that Jev frequently achieves a favorable quality--latency trade-off, offering competitive recommendation effectiveness at substantially lower observed latency than stronger LLM-based alternatives. Overall, our findings suggest that specialization for decision-making does not inherently guarantee superior recommendation quality or robustness. Instead, the practical value of decision-oriented inference depends on implementation-specific characteristics and application requirements. These findings motivate further investigation of decision-oriented systems for recommendation and other ranking tasks with structured output spaces.