Using Large Language Models (LLMs) to address the cold start problem in machine learning training data for E-commerce product listing generation

This paper focuses on the research question: 'How do Large Language Models avoid the cold start problem frequently encountered in e-commerce product listing creation? Historically, Recommendation systems use user interaction data and hence cannot come up with quality recommendations during the invention of products. Our contribution is an approach that utilizes LLMs to produce descriptive, contextually appropriate content for products without assuming the existence of user-item interactions. This shortens the time it takes to achieve optimal model performance, thus providing accurate recommendations in a shorter time.
Because of such an approach, the quality of the presented product lists increases, and the consumers feel comfortable observing products from the beginning. Further, optimizing the initial listing generation process can make training multiple cycles faster, lessen the amount of manual work, and decrease operation expenses. Finally, this work offers light to the retailers in not encountering barriers associated with recommender analysis in the early stages of accrual. It was set to enhancing the overall attractiveness, efficiency and consumer orientation in all social buying platforms.