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Instagram's OneShot retrieval framework boosts engagement and efficiency

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new retrieval framework called OneShot that aligns index learning with ranking objectives for large-scale recommendation systems. This end-to-end, in-model approach addresses the traditional misalignment between ranking accuracy and indexing efficiency. OneShot has been implemented in Instagram's short-video recommendation system, resulting in substantial improvements in user engagement and session duration. The framework also offers a 20% recall gain at operational ranking volumes and a tenfold efficiency increase at equivalent recall levels. AI

IMPACT This framework could significantly improve the efficiency and effectiveness of recommendation systems across various platforms.

RANK_REASON The cluster describes a new research paper detailing a novel framework for large-scale retrieval systems, including its deployment and performance metrics.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Instagram's OneShot retrieval framework boosts engagement and efficiency

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The cluster describes a new research paper detailing a novel framework for large-scale retrieval systems, including its deployment and performance metrics.
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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Ziwei Li, Shuyao Li, Xufeng Cai, Xue Zou, Yiming Ma, Huiting Lu, Wujie Yan, Zhichen Zhao, Yang Lu, Zhe Wang, Rui Luo, Zhengyu Su, Dan Zhang, Ji Liu ·

    OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

    arXiv:2607.27475v1 Announce Type: cross Abstract: In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this massive search effective and efficient, the syste…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ji Liu ·

    OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

    In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this massive search effective and efficient, the system relies on ranking accuracy and indexing efficien…

  3. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Ji Liu ·

    OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

    In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this massive search effective and efficient, the system relies on ranking accuracy and indexing efficien…

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