NoteLLM is a retrieval-based large language model focused on user-generated content, aiming to enhance the performance of recommendation systems. By combining topic generation with embedding generation, NoteLLM improves its ability to understand and process note content. The model adopts an end-to-end fine-tuning strategy, supporting multi-modal inputs, which enhances its application potential in diversified content domains. Its importance lies in effectively improving the accuracy of note recommendations and user experience, especially suitable for UGC platforms like Xiaohongshu.