Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or item-agnostic compression, we propose UxSID, a framework exploring a third path: semantic-group shared interest memory. By utilizing Semantic IDs (SIDs) and a dual-level attention strategy, UxSID captures target-aware preferences without the heavy cost of item-specific models. This end-to-end architecture balances computational parsimony with semantic awareness, achieving state-of-the-art performance and a 0.337% revenue lift in large-scale advertising A/B test.

翻译：建模超长用户序列需要在效率与有效性之间进行艰难权衡。当前范式依赖于逐项搜索或与项目无关的压缩，而我们提出UxSID框架，探索第三条路径：语义组共享兴趣记忆。通过利用语义ID（SID）和双层注意力策略，UxSID无需逐项模型的高昂代价即可捕获目标感知偏好。这种端到端架构在计算简洁性与语义感知之间取得平衡，实现了最先进性能，并在大规模广告A/B测试中获得0.337%的收入提升。