What question did this study set out to answer?

This research aims to explore how generative AI can improve e-commerce recommendation systems facing various challenges.

May 10, 2026Open Access

E-commerce Recommendation Systems Using Generative AI

Key Points

This research aims to explore how generative AI can improve e-commerce recommendation systems facing various challenges.
Reviewed existing recommendation systems and their limitations.
Discussed generative AI models, particularly transformer-based frameworks like GPT.
Proposed a framework for integrating Gen-AI with traditional recommendation strategies.
Identified Gen-AI as a solution for sparse data and cold-start problems.
Proposed methods enhance accuracy and contextual relevance in recommendations.
Reviewed success of generative models in creating personalized content.

Abstract

This study examines the incorporation of generative artificial intelligence (Gen-AI) into e-commerce recommendation systems. Traditional approaches, such as collaborative filtering and content-based filtering, face challenges like sparse data, cold-start issues, and changing user preferences. Gen-AI models, especially transformer-based frameworks like GPT and diffusion models, provide innovative solutions for understanding and creating personalized content. This paper reviews the progression of recommendation systems, introduces generative models, and proposes a framework that integrates Gen-AI with current recommendation strategies to enhance accuracy, diversity, and contextual relevance.

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