arXiv cs.AI· Ge Fan, Kai Meng, Huiping Chu, Yuning Jiang, Han Zhu, Bo Zheng·· 12 小时前AI 评分31
淘宝信息流推荐系统 URED：回收高分未曝光候选提升内容发现
From Waste to Discovery: Recycling High-Scored Unexposed Candidates in Taobao Feed Recommendation
AI 导读
阿里巴巴为淘宝信息流推荐上线免训练召回阶段系统 URED，将高分但从未曝光的候选回收再注入，单次请求仅一次 O(1) 查询、延迟增加不到 1%，无需重训模型。线上 A/B 测试显示发现类目数 +3.47%、内容丰富度 +8.47%，视频完播量显著提升 +2.62%，总浏览量 +0.36%（不显著）。该系统已在阿里服务数千万日活用户超过六个月，发现率达平台平均的 13.9 倍。
正文 · 原文
Abstract:Helping users discover new interests is vital to the long-term health of industrial recommender systems, yet the usual remedy, boosting exposure of under-explored (discovery) content, often backfires in production. We report URED (Unexposed Recycling for Efficient Discovery), a training-free recall-stage system serving Taobao feed recommendation at Alibaba for over six months to tens of millions of daily active users. It rests on a production-validated insight: naive boosting fails because it injects cold, information-poor candidates that rankers systematically over-estimate, buying no discovery gain while risking engagement. Items scored highly but never exposed (signals industrial pipelines routinely discard) form a pool that is both relevant and reliably predictable. Over a 30-day horizon URED aggregates them and re-injects at recall time through a lightweight pipeline spanning offline storage, distributed aggregation, and low-latency online serving, adding under 1% latency via a single O(1) lookup per request with no model retraining. Online A/B tests show +3.47% discovered categories and +8.47% content richness with engagement preserved: completion video views rise significantly (+2.62%) and total views move positive (+0.36%, not significant). Ablations confirm both design pillars are necessary: a one-day window collapses the discovery gain to +0.55% (not significant), and random injection without score filtering yields no gain while trending negative on engagement (-0.17%). Across production recall channels, URED reaches 13.9x the platform-average discovery rate (6.3x the strongest nearline baseline) at engagement quality on par with the platform average. We share deployment details and lessons for practitioners facing the exploration-exploitation dilemma under tight resource constraints.
| Subjects: | Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2611.05997 [cs.AI] |
| (or arXiv:2611.05997v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05997 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ge Fan [view email]
[v1]
Wed, 4 Nov 2026 05:45:30 UTC (529 KB)
来源：arXiv cs.AI · arxiv.org