[Submitted on 9 May 2026 (v1), last revised 9 Oct 2026 (this version, v4)]
Authors:Hongwei Zhang, Qiqiang Zhong, Junfeng Shu, Jiangxia Cao, Yiyang Lv, Huanjie Wang, Liwei Guan, Jing Yao, Yiyu Wang, Zhaojie Liu, Han Li
Abstract: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.
Submission history
From: Hongwei Zhang [view email]
[v1] Sat, 9 May 2026 16:26:48 UTC (2,829 KB)
[v2] Wed, 13 May 2026 07:20:14 UTC (2,828 KB)
[v3] Mon, 18 May 2026 07:00:17 UTC (2,828 KB)
[v4] Fri, 9 Oct 2026 08:29:36 UTC (2,829 KB)