From Logs to Language: Learning Optimal Verbalization for LLM-Based Recommendation in Production

Ali Nemati4 days ago23 sec read28 views

Researchers propose a data-centric framework using reinforcement learning to optimize how large language models convert user interaction logs into natural language inputs for recommendations, significantly improving accuracy compared to traditional template methods. This approach offers valuable insights for content creators on constructing effective context for LLM-based recommender systems.

Read the full article at arXiv cs.AI (Artificial Intelligence)

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From Logs to Language: Learning Optimal Verbalization for LLM-Based Recommendation in Production

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