Big Five inventories fail to capture meaningful differences or recover the five-factor structure in LLMs, with only 3% variance between models and four facets collapsing (r >= .92).
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Ocean4Rec uses offline LLM to create OCEAN profiles for items and time-decayed user profiles for request-time numeric reranking, improving NDCG@20 by 7.6% and 61.5% over base+recency in offline VOD evaluations.
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.
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