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UniDot's architecture unifies feature interactions and sequential modeling, achieving state-of-the-art performance in industrial recommendation tasks with unprecedented efficiency.
Hard negatives generated in real-time by LLMs can significantly enhance the training of two-tower models, outperforming traditional sampling methods and reducing popularity bias in recommendations.
