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English(EN) Self-Evolving Memory for Generative Recommendation

新方法通过演化记忆和重排序增强生成式推荐系统

研究人员开发了新的方法来改进生成式推荐系统,该系统旨在通过对用户交互序列进行建模来提供个性化推荐。一种名为 LION 的方法引入了自演化记忆范式,以解决主导用户模式掩盖不太常见模式的“演化冲突”问题。另一种方法侧重于偏好漂移感知子序列学习和分层上下文融合,以更有效、更准确地处理长序列。此外,还提出了一种通用的生成式重排序技术,通过充当验证器来增强现有推荐检索器的性能,在不重新训练检索器的情况下提高召回率。 AI

影响 这些进步可能带来跨各种平台的更准确、更高效的个性化推荐系统。

排序理由 该集群包含多篇详细介绍生成式推荐系统新方法和框架的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 6 个来源。 我们如何撰写摘要 →

新方法通过演化记忆和重排序增强生成式推荐系统

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该集群包含多篇详细介绍生成式推荐系统新方法和框架的学术论文。
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报道来源 [6]

  1. arXiv cs.LG TIER_1 English(EN) · Venkat Srinivas, Chenzhang He, Sam Woodmansee, Shawn Lian, Wenjie Hu, Renjie Jiang, Ziheng Huang, Xinyuan Zhang, Zhihao Zheng, Zhuoran Yu, Rui Li, Lei Yuan, Ziwei Li, Jimmy Jia, Mert Terzihan, Ekrem Kocaguneli, Yiming Liao, Zhichen Zhao, Yue Yin, Yue Wen… ·

    LIGE-GR:大语言模型时代从排序到生成式推荐的平滑飞跃

    arXiv:2609.18148v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and language generation share a similarity: both aim to produce a…

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

    LIGE-GR:在大语言模型时代,从排序到生成式推荐的平稳飞跃

    The remarkable success of large language models (LLMs) has provided important inspiration for the next generation of recommender systems. Structurally, recommendation and language generation share a similarity: both aim to produce an ordered sequence that optimizes the user's exp…

  3. arXiv cs.AI TIER_1 English(EN) · Xinyu Lin, Zhuosong Jiang, Zixiao Suo, Siqin Wang, Hanqing Zeng, Hanchao Yu, Yinglong Xia, Jiang Zhang, Aashu Singh, Fei Liu, Wenjie Wang, Fuli Feng, Yang Song, Qifan Wang, Tat-Seng Chua ·

    生成式推荐的自演化记忆

    arXiv:2609.15598v1 Announce Type: cross Abstract: Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evolve over time, making self-evolving an essential capability for generative recomme…

  4. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Tat-Seng Chua ·

    生成式推荐的自演化记忆

    Generative recommendation has emerged as a promising end-to-end paradigm for personalized recommendation. However, user preferences continuously evolve over time, making self-evolving an essential capability for generative recommender systems. Existing evolving strategies, such a…

  5. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Zang Li ·

    面向长序列生成式推荐的偏好漂移感知子序列学习与分层上下文融合

    Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation. Existing methods generally fall into two categories: efficient full-sequence modeling and target-aware context retrieval. Our experimen…

  6. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Neeraj Bhatia ·

    推荐检索器需要验证器:序列推荐的通用生成式重排

    First-stage recommenders in multi-stage systems produce a ranked candidate list from which a limited prefix is forwarded to downstream rankers. Because each forwarded item must be processed by more expensive ranking stages, this shortlist cannot be arbitrarily large. The first-st…