FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation
📰 ArXiv cs.AI
Learn how FOUNDv2 improves user representation with unified quantized tokenizers, enhancing personalized services on web platforms
Action Steps
- Implement FOUNDv2 using PyTorch to learn unified user quantized tokenizers
- Apply the tokenizer to integrate multi-source data for user representation
- Evaluate the performance of FOUNDv2 using metrics such as accuracy and F1-score
- Compare the storage overhead of FOUNDv2 with conventional continuous embedding methods
- Fine-tune the hyperparameters of FOUNDv2 for optimal results
Who Needs to Know This
Data scientists and AI engineers working on user representation and personalized services can benefit from this research, as it provides a novel approach to multi-source data integration and efficient storage
Key Insight
💡 FOUNDv2 overcomes limitations of conventional embedding methods with a unified paradigm for multi-source data integration and efficient storage
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🚀 FOUNDv2: Unified user quantized tokenizers for improved user representation in personalized services! 🤖
Full Article
Title: FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation
Abstract:
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
Abstract:
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
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