生成式检索 vs 稠密检索研究：静态价值先验可逆转两者差距
Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap
AI 导读
一项 CIKM EARL 2026 论文重新审视生成式检索（GR）与稠密检索（DR）在电商候选生成中的优劣：在两个 Amazon Toys 数据集上，GR 在覆盖用户的最优相关物品价值上领先，主要源于物品流行度；DR 则在精确召回和列表整体相关价值上占优。
正文 · 原文
Abstract:Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved. However, GR is not a uniformly stronger replacement of dense retrieval (DR) in online retail candidate generation. We examine their differences on two Amazon Toys datasets under different metrics. GR leads on best relevant item value among users both methods cover, while DR leads on exact-target recall and relevant value across the ranked list. With deep analysis, we find that popularity contributes the most to GR's measured value lead. Adding the same static item value to DR in one reranking step reverses this lead and keeps recall above GR. Finally, on an industrial-scale internal e-commerce dataset with LLM-judged relevance, we show that a 14B SID-LLM still underperforms a $47\times$ smaller dense encoder.
| Comments: | ACM CIKM EARL 2026 |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19065 [cs.IR] |
| (or arXiv:2610.19065v2 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19065 arXiv-issued DOI via DataCite |
Submission history
From: Renbo Tu [view email]
[v1]
Tue, 13 Oct 2026 19:02:30 UTC (227 KB)
[v2]
Wed, 4 Nov 2026 22:24:21 UTC (242 KB)
来源：arXiv cs.AI · arxiv.org