Precision-Driven Product Recommendations Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Published Paper

• Journal: Software | An Open Access Journal from MDPI
• Title: Precision-Driven Product Recommendations Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems
• DOI: https://doi.org/10.3390/software3010004

Authors

• Konstantinos I. Roumeliotis
• Prof. Nikolaos D. Tselikas
• Prof. Dimitrios K. Nasiopoulos

Abstract

This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

Keywords

recommender systems; recommender system algorithms; product recommendation; product recommendation algorithms; GPT model; k-means clustering; content-based filtering; hierarchical clustering; recommender systems evaluation; model evaluation