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When LLM-Based User Profiling Adds Value in Production Streaming Recommendation
Personalized recommendation depends critically on how user representations are constructed from historical behavior. Two paradigms have emerged for constructing semantic user profiles in content-based recommendation. First, aggregate methods derive user representations as numerical aggregates of sem
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