From Waste to Discovery: Recycling High-Scored Unexposed Candidates in Taobao Feed Recommendation

Authors: Ge Fan , Kai Meng , Huiping Chu , Yuning Jiang , Han Zhu , Bo Zheng

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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

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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)

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