UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence

UxSID captures target-aware preferences via semantic-group shared interest memory and dual-level attention, achieving state-of-the-art results and 0.337% revenue lift.

Hongwei Zhang, qiqiang zhong, Jiangxia Cao, Junfeng Shu, Yiyang Lv, Huanjie Wang, Liwei Guan, Jing Yao, Yiyu Wang, Liu Zhaojie, Han Li

Published 2026Sydney Poster Session 5 · Thu, Dec 10, 10:00 AM–1:00 PM local time · Hall 1-4arXiv ↗OpenReview ↗

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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.