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Related Questions
- How do large language models use user embeddings to make personalized recommendations when user data is scarce?
- Can you explain the role of user embeddings in improving recommendation algorithms for users with limited interaction history?
- How do large language models generate user embeddings to represent users with sparse interaction data?
- What techniques are used to fine-tune user embeddings for improved recommendation accuracy in low-data scenarios?
- How do large language models handle cold start problems when dealing with new users or items with no interaction data?
- Can you discuss the impact of user embedding dimensionality on recommendation performance in sparse user data?
- What are some strategies for leveraging user embeddings to improve recommendation diversity and novelty in low-data environments?
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