This paper explores the effectiveness of ChatGPT as a conversational recommendation system, focusing on refining recommendations through reprompting with user feedback. The authors propose a pipeline that simulates how users might interact with ChatGPT to obtain recommendations, starting with initial prompts and then refining them based on feedback. They also investigate the impact of popularity bias in ChatGPT's recommendations and compare its performance to baseline models. The study finds that reprompting ChatGPT with feedback significantly improves the relevance of recommendations, and that popularity bias can be mitigated through prompt engineering. The research uses the HetRec2011 dataset, which includes movie information from multiple sources. The authors analyze how different levels of content affect the similarity of recommendations and find that higher content levels lead to more accurate recommendations. They also evaluate the performance of ChatGPT in different scenarios, including direct recommendations and reprompting with feedback. The results show that ChatGPT performs better than random and traditional recommendation systems, particularly when reprompting is used. The study also examines the impact of popularity bias in recommendations and proposes strategies to mitigate it. The authors find that ChatGPT tends to recommend popular items more frequently, but this can be reduced by adjusting parameters such as temperature and prompt settings. The results indicate that using a higher temperature and restricting recommendations to less popular items increases the novelty of recommendations. Overall, the paper demonstrates that ChatGPT can be an effective conversational recommendation system when reprompting with feedback is used. The authors conclude that ChatGPT's robust domain knowledge and ability to adapt to user feedback make it a promising tool for recommendation systems. However, the study also highlights limitations, such as the need for large text data and the use of relatively old movie data. Future work could involve using more recent datasets and comparing ChatGPT with other LLMs and recommendation algorithms.This paper explores the effectiveness of ChatGPT as a conversational recommendation system, focusing on refining recommendations through reprompting with user feedback. The authors propose a pipeline that simulates how users might interact with ChatGPT to obtain recommendations, starting with initial prompts and then refining them based on feedback. They also investigate the impact of popularity bias in ChatGPT's recommendations and compare its performance to baseline models. The study finds that reprompting ChatGPT with feedback significantly improves the relevance of recommendations, and that popularity bias can be mitigated through prompt engineering. The research uses the HetRec2011 dataset, which includes movie information from multiple sources. The authors analyze how different levels of content affect the similarity of recommendations and find that higher content levels lead to more accurate recommendations. They also evaluate the performance of ChatGPT in different scenarios, including direct recommendations and reprompting with feedback. The results show that ChatGPT performs better than random and traditional recommendation systems, particularly when reprompting is used. The study also examines the impact of popularity bias in recommendations and proposes strategies to mitigate it. The authors find that ChatGPT tends to recommend popular items more frequently, but this can be reduced by adjusting parameters such as temperature and prompt settings. The results indicate that using a higher temperature and restricting recommendations to less popular items increases the novelty of recommendations. Overall, the paper demonstrates that ChatGPT can be an effective conversational recommendation system when reprompting with feedback is used. The authors conclude that ChatGPT's robust domain knowledge and ability to adapt to user feedback make it a promising tool for recommendation systems. However, the study also highlights limitations, such as the need for large text data and the use of relatively old movie data. Future work could involve using more recent datasets and comparing ChatGPT with other LLMs and recommendation algorithms.