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English(EN) HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders

HiSAC框架赋能推荐系统高效超长序列建模

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了分层稀疏激活压缩（HiSAC）框架，该框架旨在使超长序列建模在推荐系统中变得可行。HiSAC通过将交互编码为多级语义ID并使用分层投票机制激活个性化兴趣代理来解决现有方法的局限性。这种方法允许细粒度的偏好中心，并使用Soft-Routing Attention高效地聚合历史信号，从而最大限度地减少量化误差并保留长尾偏好。在淘宝“猜你喜欢”功能上部署HiSAC后，该框架展示了显著的压缩和成本降低效果，点击率提高了1.65%。 AI

影响 通过处理更长的用户行为序列，实现更高效和个性化的推荐。

排序理由 发布关于序列建模新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

HiSAC框架赋能推荐系统高效超长序列建模

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发布关于序列建模新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao, Binbin Cao, Jian Wu, Yuning Jiang ·

    HiSAC：用于超长序列推荐模型的分层稀疏激活压缩

    arXiv:2602.21009v2 Announce Type: replace-cross Abstract: Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due to latency and memory constraints. While summarizing history vi…

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