This paper outlines a comprehensive framework for integrating Large Language Models (LLMs) into e-commerce product page recommendation systems. This research begins by presenting the problem statement, highlighting how traditional recommendation approaches struggle to capture the rich semantic nuances embedded in diverse textual sources such as product descriptions, user reviews, and Q&A content. This study then review relevant advancements in recommender systems and natural language processing, setting the stage for our proposed solution. Our architecture features a transformer-based embedding pipeline that encodes text into meaningful representations, an LLM-driven recommendation core that enhances personalization and relevance, and real-time inference strategies to ensure low-latency responses. To provide a practical view, we walk through the end-to-end flow of a user query—from the moment it is entered to the final personalized product suggestions displayed. This study concludes with an analysis of key challenges such as scalability, bias, and privacy considerations, along with future directions to further optimize LLM-based recommendation systems for robust real-world performance.

LLM Transformer Models Personalized E-commerce Product Recommendations Semantic Analysis