  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.
