Towards Advancing Text-Based User and Item Representation in Personalized Recommendation

In the realm of personalized recommendation systems, accurately capturing user preferences and item characteristics is important for delivering relevant and satisfying recommendations. This study introduces innovative approaches using Large Language Models (LLMs) to generate detailed textual descriptions that enhance both user and item representations. We propose a dual strategy: for user representation, we employ supervised fine-tuning coupled with Retrieval-Augmented Generation (RAG) to keep the model current with dynamic user preferences; for item representation, we leverage the extensive knowledge base of LLMs to enrich item descriptions and infer traits from user interactions. These methods promise a deeper, more nuanced understanding of both users and items, potentially leading to superior recommendation accuracy. We adopt a rigorous evaluation methodology, ensuring the reliability of our results and the effectiveness of our proposed system. This paper discusses these methodologies, presents our preliminary findings, and highlights the potential of text-augmented profiles in advancing recommendation systems.