English(EN) Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling

新框架对长用户序列进行建模以实现视频推荐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-09 04:00

研究人员开发了一个新框架，用于对短视频推荐系统中极长的用户行为序列进行建模。该系统使用内容原生语义ID代替传统的物品ID，以减小嵌入表大小并提高对新内容的泛化能力。此外，一个全局感知压缩Transformer可以压缩用户序列，显著降低内存和计算需求。 AI

影响通过处理更长的用户历史记录，从而在短视频平台中实现更有效的个性化。

排序理由学术论文，详细介绍了特定应用领域的新技术框架。[lever_c_demoted from research: ic=1 ai=1.0]

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

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了特定应用领域的新技术框架。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 123 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

arXiv cs.AI TIER_1 English(EN) · Ruixiao Sun, Diego Uribe Mora, Zhimeng Jiang, Yuanzhen Lin, Jiarui Wang, Yuening Li, Danfeng Guo, Zhizhong Chen, Chuan He, Liang Liu · 2026-06-09 04:00

超越商品ID：通过语义原生长序列建模扩展短视频推荐

arXiv:2606.07546v1 Announce Type: cross Abstract: Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by two bottlenecks: the semantic sparsity of atomic Video IDs and the quadratic comp…