Traditional Recommender

Traditional recommender systems aim to predict user preferences and suggest relevant items, primarily leveraging collaborative filtering techniques to identify patterns in user-item interactions. Current research focuses on enhancing these systems by integrating large language models (LLMs) to incorporate semantic information from item descriptions and user reviews, and by employing graph neural networks to model relationships between users and items more effectively. These advancements aim to address limitations of traditional methods, such as cold-start problems and popularity bias, leading to more accurate, diverse, and personalized recommendations with significant implications for e-commerce, entertainment, and other applications.