#artificial intelligence #machine learning Preprint Open access

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

Hongwei Zhang Qiqiang Zhong Junfeng Shu Jiangxia Cao Yiyang Lv Huanjie Wang Liwei Guan Jing Yao Yiyu Wang Zhaojie Liu Han Li

Oct 2026

Artificial Intelligence Machine Learning

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.

DOI arXiv

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