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English(EN) Separating Thinking from Spelling for Efficient Generative Recommendation

Deco方法将生成式推荐吞吐量提升3.2-8.6倍

研究人员开发了一种名为Deco的新方法,用于生成式推荐系统,该方法将“思考”过程与项目标识符的“拼写”分离开来。这种方法允许系统将用户历史浓缩成一个单一的概念,从而使轻量级解码器能够高效地生成推荐。Deco在各种数据集上实现了比标准方法显著更高的吞吐量,速度提升了3.2-8.6倍,同时保持了相当或更好的准确性。这种效率使得更大的候选池和更强大的LLM骨干模型在推荐任务中变得可行。 AI

影响 该方法可以显著提高推荐系统的效率和可扩展性,从而以更低的延迟实现更个性化的建议。

排序理由 该集群包含一篇详细介绍生成式推荐系统新技术方法的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

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

Deco方法将生成式推荐吞吐量提升3.2-8.6倍

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该集群包含一篇详细介绍生成式推荐系统新技术方法的学术论文。
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报道来源 [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Evgeny Frolov ·

    将思考与拼写分离以实现高效生成式推荐

    Generative recommenders retrieve items by writing their semantic IDs (SIDs) code by code under trie-constrained beam search, an approach now deployed on large web platforms. Standard autoregressive interfaces pay for the SID length K twice: a history of T items enters the backbon…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Evgeny Frolov ·

    将思考与拼写分离以实现高效生成式推荐

    Generative recommenders retrieve items by writing their semantic IDs (SIDs) code by code under trie-constrained beam search, an approach now deployed on large web platforms. Standard autoregressive interfaces pay for the SID length K twice: a history of T items enters the backbon…