A Quality Control and Evaluation Framework for Generative AI in Personalized E-Commerce Product Descriptions

Abstract

This research introduces a comprehensive quality control and evaluation framework tailored for generative AI applications in personalized e-commerce product descriptions. The framework addresses the critical need for ensuring the relevance, accuracy, fluency, and persuasiveness of AI-generated content, which directly impacts customer engagement and sales conversion rates. It incorporates multi-faceted metrics, including semantic similarity, content diversity, factual correctness, and user perception, to assess the quality of generated descriptions. Furthermore, the framework employs a feedback loop mechanism that continuously refines the AI models based on real-time performance data and user interactions. Through rigorous experimentation and comparative analysis with existing methods, we demonstrate the effectiveness of the proposed framework in producing high-quality, personalized product descriptions that enhance the e-commerce shopping experience. The study also explores the ethical considerations surrounding the use of AI in marketing and provides guidelines for responsible AI deployment.