Hybrid GPT and Neural Models for Personalized E-Commerce: A Novel Framework for Adaptive and Transparent Product Recommendations

Abstract

In order to transform e-commerce customization, the study presents a product recommendation system that makes use of OpenAI's GPT model's better resources in conjunction with neural integration. Traditional systems often rely on collaborative filtering or content-based models. This may lack precision and adaptability to individual needs. The proposed method overcomes these limitations by integrating hierarchical classification. Hybrid similarity scoring and GPT-based annotation capabilities provide more precise and interpretable rules. The multi-step technique ensures that the machine leads to different types of statistics, which include textual product descriptions and generated metadata to gain meaningful insights. This is because pre-processing techniques determine the specific format of numbers and facts, while embeddings built from neural models provide rich semantic knowledge about relationships between products. The weighted scoring mechanism dynamically adjusts to the choices made by individuals. This increases the machine's capability to generalize to the needs of a man or woman. In addition, GPT-inspired factors promote consideration and transparency in explaining the underlying reasons in natural language. The results of the experiment validated the device's ability to offer advanced customization and user satisfaction. By combining domain-specific data with existing IA strategies, this structure sets a new benchmark for precision-driven, people-centric ecommerce applications.