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LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation
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LIKR is a unified framework where an LLM is treated as a reasoner that outputs intuitive exploration strategies for a Knowledge Graph (KG). It integrates LLM's text-based output (user preferences) with KG embeddings into a hybrid reward function for a reinforcement learning agent. This agent then performs KG path reasoning to make recommendations, synergizing both LLM intuition and KG structure for enhanced reasoning capabilities.
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