Recommender systems aim to personalize user experiences by suggesting relevant items based on historical interactions and preferences. However, traditional CoT reasoning struggles to align generative agents’ outputs with real-time user preferences, especially when faced with sparse or changing data. In this paper, we introduced the Meta-CoT-A*-MCTS framework by utilizing A* for deterministic pathfinding and MCTS for stochastic exploration, which combines the strengths of A* search and MCTS to improve personalized recommendation systems. The hybrid approach allows for dynamic exploration of reasoning paths, optimizing the balance between computational efficiency and recommendation quality. Our experiments on datasets including MovieLens-1M, MovieLens-10M, Amazon-Book, and Steam demonstrate that that Meta-CoT-A*-MCTS consistently outperforms other methods, achieving higher F1 scores across various benchmarks and model sizes. This framework effectively balances high-quality recommendation generation with computational efficiency, reducing time complexity while maintaining high-quality recommendations.