LERL combines an LLM high-level planner for diverse semantic categories with a low-level RL policy for item selection to improve long-term user satisfaction and reduce content homogeneity in interactive recommenders.
In: Proceedings of the 15th ACM Confer- ence on Recommender Systems
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LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendation
LERL combines an LLM high-level planner for diverse semantic categories with a low-level RL policy for item selection to improve long-term user satisfaction and reduce content homogeneity in interactive recommenders.