LLM Based Recommender System

Large language models (LLMs) are being integrated into recommender systems to leverage their advanced natural language understanding and reasoning capabilities, aiming to improve recommendation accuracy and personalization. Current research focuses on mitigating biases inherent in LLMs, enhancing the modeling of complex user-item interactions (including high-order interactions and temporal dynamics), and developing efficient tokenization and retrieval strategies for seamless LLM integration. This burgeoning field promises to significantly advance recommender system performance, particularly in addressing data sparsity and cold-start problems, leading to more relevant and engaging user experiences across various applications.