Exploring the Depths of User Interest: How LLMs are Revolutionizing Personalized Shopping

This paper explores the transformative impact of Large Language Models (LLMs) on personalized shopping, highlighting their revolutionary capabilities in natural language understanding, dynamic interest modeling, and multimodal interaction. It discusses how LLMs address the limitations of traditional recommendation systems by enabling semantic deconstruction of user queries, real-time strategy updates, and scenario-based solutions. The study reviews the evolution of LLMs and personalized recommendation systems, emphasizing breakthroughs in contextual reasoning and cross-modal feature fusion. Practical implementation strategies are proposed, including lightweight technology deployment, cross-departmental collaboration, and user-centric experience design. The paper concludes by underscoring the dual technical and commercial value of LLMs, advocating for future research in causal reasoning and multimodal evaluation to enhance algorithmic fairness and sustainability.