Query as Anchor: Scenario-Adaptive User Representation via Large Language Model

• URL: http://arxiv.org/abs/2602.14492v2
• Date: Tue, 17 Feb 2026 02:44:08 GMT
• Title: Query as Anchor: Scenario-Adaptive User Representation via Large Language Model
• Authors: Jiahao Yuan, Yike Xu, Jinyong Wen, Baokun Wang, Ziyi Gao, Xiaotong Lin, Yun Liu, Xing Fu, Yu Cheng, Yongchao Liu, Weiqiang Wang, Zhongle Xie,
• Abstract summary: We propose Query-as-Anchor, a framework shifting user modeling from static encoding to dynamic, query-aware synthesis.<n>We first construct UserU, an industrial-scale pre-training dataset that aligns behavioral sequences with user understanding semantics.<n>We introduce Cluster-based Soft Prompt Tuning to enforce discriminative latent structures.<n>For deployment, anchoring queries at sequence termini enables KV-cache-accelerated inference with negligible incremental latency.
• Score: 28.30329175937291
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial-scale user representation learning requires balancing robust universality with acute task-sensitivity. However, existing paradigms primarily yield static, task-agnostic embeddings that struggle to reconcile the divergent requirements of downstream scenarios within unified vector spaces. Furthermore, heterogeneous multi-source data introduces inherent noise and modality conflicts, degrading representation. We propose Query-as-Anchor, a framework shifting user modeling from static encoding to dynamic, query-aware synthesis. To empower Large Language Models (LLMs) with deep user understanding, we first construct UserU, an industrial-scale pre-training dataset that aligns multi-modal behavioral sequences with user understanding semantics, and our Q-Anchor Embedding architecture integrates hierarchical coarse-to-fine encoders into dual-tower LLMs via joint contrastive-autoregressive optimization for query-aware user representation. To bridge the gap between general pre-training and specialized business logic, we further introduce Cluster-based Soft Prompt Tuning to enforce discriminative latent structures, effectively aligning model attention with scenario-specific modalities. For deployment, anchoring queries at sequence termini enables KV-cache-accelerated inference with negligible incremental latency. Evaluations on 10 Alipay industrial benchmarks show consistent SOTA performance, strong scalability, and efficient deployment. Large-scale online A/B testing in Alipay's production system across two real-world scenarios further validates its practical effectiveness. Our code is prepared for public release and will be available at: https://github.com/JhCircle/Q-Anchor.

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