Learning Unified User Quantized Tokenizers for User Representation

• URL: http://arxiv.org/abs/2508.00956v2
• Date: Tue, 30 Sep 2025 01:51:32 GMT
• Title: Learning Unified User Quantized Tokenizers for User Representation
• Authors: Chuan He, Yang Chen, Wuliang Huang, Tianyi Zheng, Jianhu Chen, Bin Dou, Yice Luo, Yun Zhu, Baokun Wang, Yongchao Liu, Xing Fu, Yu Cheng, Chuntao Hong, Weiqiang Wang, Xin-Wei Yao, Zhongle Xie,
• Abstract summary: U2QT (Unified User Quantized Tokenizers) is a novel framework that integrates cross-domain knowledge transfer with early fusion of heterogeneous domains.<n>Our framework employs a two-stage architecture: first, we use the Qwen3 Embedding model to derive a compact yet expressive feature representation.<n>Second, a multi-view RQ-VAE discretizes causal embeddings into compact tokens through shared and source-specific codebooks.
• Score: 32.942924291891636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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