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Taobao's URED system boosts content discovery by recycling unexposed items

By PulseAugur Editorial · [2 sources] ·

Researchers have developed URED (Unexposed Recycling for Efficient Discovery), a novel system designed to improve content discovery in recommender systems like Taobao's. Unlike traditional methods that boost under-explored content, URED focuses on recycling high-scored but unexposed items. This approach avoids the engagement risks associated with injecting cold, information-poor candidates. Deployed on Taobao for over six months, URED has demonstrated significant improvements in discovered categories and content richness without compromising user engagement, all while adding minimal latency and requiring no model retraining. AI

IMPACT This research offers a practical method for improving content discovery in large-scale recommender systems without extensive retraining or significant latency increases.

RANK_REASON The cluster describes a research paper detailing a new system for recommender systems, including performance metrics and deployment details.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Taobao's URED system boosts content discovery by recycling unexposed items

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The cluster describes a research paper detailing a new system for recommender systems, including performance metrics and deployment details.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ge Fan, Kai Meng, Huiping Chu, Yuning Jiang, Han Zhu, Bo Zheng ·

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

    arXiv:2611.05997v1 Announce Type: new 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 (U…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Bo Zheng ·

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

    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 t…

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