UxSID：面向超长序列的语义感知用户兴趣建模
UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
AI总结：
本文提出UxSID框架，通过语义组共享兴趣记忆和双层注意力策略，实现高效且语义感知的超长用户序列建模，取得最佳性能并提升广告收益。
摘要：
建模超长用户序列涉及效率与效果之间的艰难权衡。尽管当前方法依赖于物品特定搜索或物品无关压缩，我们提出UxSID，探索第三种路径：语义组共享兴趣记忆。通过利用语义ID（SIDs）和双层注意力策略，UxSID在不付出物品特定模型高昂代价的情况下捕捉目标感知偏好。这种端到端架构在计算效率与语义感知之间取得平衡，实现了最先进的性能，并在大规模广告A/B测试中提升了0.337%的收益。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
