This paper aims to address the challenge of sparse and missing data in recommendation systems, a significant hurdle in the age of big data. Traditional imputation methods struggle to capture complex relationships within the data. We propose a novel approach that fine-tune Large Language Model (LLM) and use it impute missing data for recommendation systems. LLM which is trained on vast amounts of text, is able to understand complex relationship among data and intelligently fill in missing information. This enriched data is then used by the recommendation system to generate more accurate and personalized suggestions, ultimately enhancing the user experience. We evaluate our LLM-based imputation method across various tasks within the recommendation system domain, including single classification, multi-classification, and regression compared to traditional data imputation methods. By demonstrating the superiority of LLM imputation over traditional methods, we establish its potential for improving recommendation system performance.

翻译：本文旨在解决推荐系统中数据稀疏与缺失的挑战，这是大数据时代面临的一个重大难题。传统的数据插补方法难以捕捉数据内部的复杂关系。我们提出一种新颖的方法，通过微调大语言模型并将其用于推荐系统的缺失数据插补。大语言模型经过海量文本训练，能够理解数据间的复杂关系并智能地填充缺失信息。推荐系统随后利用这些增强后的数据生成更准确、更个性化的建议，最终提升用户体验。我们在推荐系统领域的多项任务中评估了基于大语言模型的插补方法，包括二分类、多分类和回归任务，并与传统数据插补方法进行了比较。通过证明大语言模型插补方法相较于传统方法的优越性，我们确立了其在提升推荐系统性能方面的潜力。