arXiv:2508.00956v3 Announce Type: replace-cross Abstract: User representation learning serves as a fundamental pillar for personalized services on large-scale web platforms. Despite its importance, conventional continuous embedding methods face significant challenges, including the lack of a unified paradigm for multi-source data integration, prohibitive storage overhead due to low information density, and the lack of multi-scale modeling granularity. To overcome these limitations, we introduce FOUNDv2, a comprehensive user representation scheme centered on the Unified User Quantized Tokenizer
The proliferation of AI-driven personalized services on large-scale web platforms demands more efficient and unified user representation methods.
This development addresses critical challenges in user representation, offering a unified, high-density, and multi-scale approach that could significantly improve the efficiency and effectiveness of personalized AI services.
Existing continuous embedding methods, prone to high storage overhead and fragmented multi-source data integration, are being supplanted by quantized tokenizers that offer more unified and granular user models.
- · Large web platforms
- · AI service providers
- · Data scientists
- · Recommendation engine developers
- · Legacy continuous embedding methods
- · Companies with inefficient data storage architectures
- · Specialized single-source user modeling approaches
Improved personalization and efficiency in AI-powered services across web platforms.
Reduced operational costs for platforms due to lower storage overhead and more efficient user data processing.
The development of new AI applications and user-centric features previously constrained by limitations in user representation.
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Read at arXiv cs.AI