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Personalization for social robots is stymied not by technology but by the complex data landscape surrounding children's interactions and needs.
Fine-tuned behavioral models can achieve superior population-level alignment, closing the gap with general-purpose models in individual predictions.
Generative recommendation models can amplify popularity bias due to imbalanced tokenization, but a simple codebook rebalancing strategy can significantly improve performance.