Citation:

Aditi Choudhary, 2024. "Enhanced Recommendations Based on Health in E-Commerce Using Large Language Models", ESP Journal of Engineering & Technology Advancements  4(4): 161-165.

Abstract:

The rapid growth and increasing complexity of e-commerce platforms have driven the need for innovative methods to enhance product recommendations. This research builds on earlier studies that utilized machine learning algorithms and investigates how Large Language Models (LLMs) can further improve recommendation systems. By leveraging their superior natural language comprehension, LLMs can provide personalized recommendations tailored to individual customer inquiries, purchase histories, and product information. In this study, we propose a framework that integrates LLMs using Amazon Bedrock to optimize product recommendations in e-commerce. Our approach emphasizes understanding user intent dynamically and enhancing recommendation accuracy across different product categories. Additionally, the system includes a comprehensive knowledge base that adheres to established rules and regulations related to product recommendations, taking into account individual user health profiles. The findings reveal a significant increase in recommendation precision and overall customer satisfaction, highlighting the transformative potential of LLMs in reshaping recommendation strategies within the e-commerce sector.

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Keywords:

E-Commerce, Recommendation System, Large Language Models, Machine Learning, Amazon Bedrock, Personalization.