Dynamic user representations adapt to any scenario using query-anchored LLM embeddings.
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.
Query-as-Anchor generates dynamic, scenario-specific user representations from a single pre-trained model instead of static embeddings.
UserU dataset combines behavioral interaction data with LLM-generated synthetic query-answer pairs grounded in actual user behavior.
Hierarchical coarse-to-fine encoder processes six modalities of user behavior into multi-level embeddings for improved representation.
Traditional static embeddings lack flexibility to adapt representations across different business scenarios and downstream tasks.
Dense language-centric LLM training data has significant semantic gap with sparse, symbolic, heterogeneous user behavior logs.
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