PO4ISR[Sun et.al.,2023] , a self-reflection prompt generation mechanism is adopted to improve the understanding of user behavior patterns of large language models (LLM), so as to more accurately recommend the next point of interest. It mainly includes the following steps: Initial prediction and interpretation ⚫ Using the initial prompt, ask the LLM to predict the user's top 10 possible POIs and provide an explanation for each prediction. Error collection LLM Self Reflection ⚫ The LLM analyzes and automatically generates r causes that may lead to the failure of the prediction, and integrates these cause sets R into the optimization process. Error-based prompt refinement. ⚫ Based on the above reasons and real interest point Promptr,(i) LLM re-adjusts the initial prompt: 1 2 3 4