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：建模超长用户序列在效率与效果之间面临难以调和的权衡。现有范式依赖基于特定物品的搜索或忽略物品的压缩，而本文提出UxSID——一种探索第三条路径的框架：语义分组共享兴趣记忆。通过利用语义ID（SIDs）和双层注意力策略，UxSID无需承担特定物品模型的高昂代价，即可捕捉到目标感知的偏好。这种端到端架构在计算简洁性与语义感知能力间取得平衡，实现了最先进的性能，并在大规模广告A/B测试中带来0.337%的收入提升。