Jev 决策导向模型推荐重排序实证研究：与 Qwen 重排序器的质量-延迟权衡对比

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Abstract:Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation quality and serving efficiency. We investigate whether a decision-oriented model provides a useful alternative when the reranking task is fundamentally a structured choice among predefined candidate items. Specifically, we conduct a controlled empirical study of Jev, described by TypeSafe AI as a ``System One Model,'' for personalized recommendation reranking and compare it with recommendation-specific models and pointwise and listwise Qwen rerankers across multiple Amazon Reviews domains and candidate-set sizes, evaluating both recommendation effectiveness and observed serving latency. Our results show that Jev maintains strong recommendation effectiveness relative to the evaluated baselines while exhibiting substantially more gradual latency growth than the pointwise Qwen rerankers, although its observed serving latency remains substantially higher than that of recommendation-specific models. Together, these characteristics place Jev in a distinct quality--latency operating regime across candidate sizes and domains. These findings motivate further investigation of decision-oriented models for recommendation and other ranking tasks with structured output spaces.

Subjects:	Information Retrieval (cs.IR); Computation and Language (cs.CL)
Cite as:	arXiv:2609.40241 [cs.IR]
	(or arXiv:2609.40241v1 [cs.IR] for this version)
	https://doi.org/10.48550/arXiv.2609.40241 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hanjia Lyu [view email]
[v1] Wed, 30 Sep 2026 17:33:43 UTC (41 KB)