User representation learning is critical for personalization on large-scale web platforms, but conventional continuous embedding methods suffer from high storage overhead and lack of unified multi-source integration. A new framework, FOUNDv2, introduced in an arXiv paper by researchers including He, Chuan, Yang, Dou, Bin, and others, addresses these limitations with a Unified User Quantized Tokenizer (U2QT) that converts diverse user data into a standardized discrete token space.
"User representation learning serves as a fundamental pillar for personalized services on large-scale web platforms." — from the paper's abstract
The Limitations of Continuous Embeddings
According to the paper, traditional continuous embedding methods face three major challenges. First, there is no unified paradigm for integrating data from multiple sources, leading to fragmented representations. Second, the low information density of continuous embeddings results in "prohibitive storage overhead." Third, these methods lack multi-scale modeling granularity, making it difficult to capture both fine-grained behavioral dependencies and macro-temporal periodicity.
How FOUNDv2 Works
FOUNDv2 introduces a robust two-stage architecture. The first stage extracts compact feature representations from heterogeneous user data. The second stage employs a multi-view Residual Quantized Variational Autoencoder (RQ-VAE) to discretize these representations into storage-efficient tokens. The model uses both shared and source-specific codebooks to balance universal and domain-specific patterns. To imbue the tokens with predictive intelligence, the framework incorporates multi-scale alignment objectives that capture fine-grained behavioral dependencies and macro-temporal periodicity.
Performance and Deployment Results
The authors report that FOUNDv2 "consistently outperforms task-specific baselines while achieving substantial reductions in storage and computational costs" across various benchmarks. The large-scale deployment of FOUNDv2 on Alipay validates its practical scalability and efficiency in diverse industrial scenarios. Code is available at the provided URL.
Comparative Overview
| Aspect | Continuous Embedding Methods | FOUNDv2 (Quantized Tokenizer) |
|---|---|---|
| Data Integration | No unified paradigm | Unified via U2QT framework |
| Storage Efficiency | High overhead | Substantial reduction |
| Modeling Granularity | Single scale | Multi-scale (behavioral + temporal) |
| Benchmark Performance | Task-specific baselines | Consistently outperforms |
| Real-World Deployment | Not specified | Deployed on Alipay |
Implications for Enterprise Technology Leaders
For CTOs and digital transformation leaders, FOUNDv2 represents a shift toward more efficient and scalable user representation. The ability to dramatically reduce storage costs while improving personalization accuracy can lower infrastructure expenses and enhance user experiences on platforms with millions of users. The successful deployment on Alipay (a major fintech platform) indicates readiness for production environments. While the paper focuses on web platforms, the underlying principles of discrete tokenization and multi-source integration are directly applicable to any domain requiring unified user modeling, including supply chain personalization and trade finance platforms.