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

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.

Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)

arXiv:2605.09040 [cs.AI]

(or arXiv:2605.09040v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.09040 Focus to learn more arXiv-issued DOI via DataCite

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