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Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
Foundation model recommender systems require user context that can be consumed by large language models, reasoned over, and refined through natural-language interaction. Traditional behavioral embedding vectors remain highly effective for retrieval and ranking, but they are opaque to users and not n
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