Recommendation systems are everywhere, from suggesting movies to curating job listings. But what if these systems aren't treating all users equally? A new research paper explores how AI models, especially Large Language Models (LLMs) like GPT, can be used to make recommendations fairer for everyone. Traditional recommendation systems often favor "active" users—those who interact frequently. This leaves "inactive" or "weak" users with less relevant suggestions. The researchers propose a clever two-phase solution. First, they identify these "weak" users by analyzing their activity and the quality of recommendations they receive. Then, they use the power of LLMs to understand these users' preferences more deeply. Think of it like this: instead of relying on limited past activity, the LLM gets a detailed "instruction manual" of each weak user's likes and dislikes. This helps the system generate much better recommendations, even for those who haven't interacted much. The results are promising. Experiments show that this hybrid approach significantly improves the quality of recommendations for weak users, boosting overall system fairness. This research is a big step towards building more inclusive and equitable AI systems. It shows how we can combine the strengths of different AI models to create a better experience for all users, regardless of their activity level. The future of AI recommendations is not just about accuracy, but also about fairness and inclusivity. This research paves the way for a more equitable online experience for everyone.