Data Imputation using Large Language Model to Accelerate Recommendation System

TL;DR

This paper introduces a novel data imputation method using fine-tuned Large Language Models to address data sparsity in recommendation systems, leading to more accurate and personalized suggestions.

Contribution

It is the first to leverage fine-tuned LLMs for data imputation in recommendation systems, demonstrating improved performance over traditional methods.

Findings

LLM-based imputation outperforms traditional methods in accuracy

Enhanced recommendation quality with richer data

Applicable across classification and regression tasks

Abstract

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.