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English(EN) Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval

DINOSAUR框架通过纳入嵌入不确定性来增强检索

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了DINOSAUR，一个用于近似最近邻（ANN）搜索的新框架，该框架解决了检索系统中嵌入不确定性的问题。传统方法使用用户和项目嵌入的单点估计，导致偏向热门项目并忽略了长尾的利基内容。DINOSAUR通过为每个项目和用户采样多个嵌入来纳入嵌入不确定性，从而实现更全面的搜索，在召回损失最小的情况下提高覆盖率。 AI

影响 通过更好地处理嵌入中的不确定性来改进推荐系统，可能增加利基内容的发现。

排序理由 该集群包含一篇详细介绍近似最近邻搜索新方法的论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

DINOSAUR框架通过纳入嵌入不确定性来增强检索

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该集群包含一篇详细介绍近似最近邻搜索新方法的论文。
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2 independent sources
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Olivier Jeunen ·

    面向不确定性感知检索的分布近似最近邻搜索

    arXiv:2606.04603v1 Announce Type: cross Abstract: Approximate Nearest Neighbour search indices form the backbone of real-world recommender systems, enabling real-time candidate retrieval over million-item catalogues. Typically, a single point estimate embedding is learnt for ever…

  2. arXiv stat.ML TIER_1 English(EN) · Olivier Jeunen ·

    面向不确定性感知检索的分布近似最近邻搜索

    Approximate Nearest Neighbour search indices form the backbone of real-world recommender systems, enabling real-time candidate retrieval over million-item catalogues. Typically, a single point estimate embedding is learnt for every user and every item. At serving time, the user e…

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