Learning Unified User Quantized Tokenizers for User Representation

TL;DR

U2QT introduces a unified framework for user representation that combines cross-domain knowledge transfer with early fusion, utilizing quantized tokens for efficient storage and improved performance across multiple downstream tasks.

Contribution

The paper presents U2QT, a novel two-stage architecture that integrates cross-domain transfer and early fusion using quantized tokenizers, addressing scalability and generalization issues in user modeling.

Findings

Outperforms task-specific baselines in behavior prediction and recommendation.

Achieves significant storage and computation efficiency.

Supports seamless integration with language models for industrial applications.

Abstract

Multi-source user representation learning plays a critical role in enabling personalized services on web platforms (e.g., Alipay). While prior works have adopted late-fusion strategies to combine heterogeneous data sources, they suffer from three key limitations: lack of unified representation frameworks, scalability and storage issues in data compression, and inflexible cross-task generalization. To address these challenges, we propose U2QT (Unified User Quantized Tokenizers), a novel framework that integrates cross-domain knowledge transfer with early fusion of heterogeneous domains. Our framework employs a two-stage architecture: first, we use the Qwen3 Embedding model to derive a compact yet expressive feature representation; second, a multi-view RQ-VAE discretizes causal embeddings into compact tokens through shared and source-specific codebooks, enabling efficient storage while maintaining semantic coherence. Experimental results showcase U2QT's advantages across diverse downstream tasks, outperforming task-specific baselines in future behavior prediction and recommendation tasks while achieving efficiency gains in storage and computation. The unified tokenization framework enables seamless integration with language models and supports industrial-scale applications.