Conventional Recommendation

Conventional recommendation systems aim to predict user preferences and provide relevant items, but face challenges in adapting to dynamic user behavior and incorporating contextual information. Current research focuses on integrating large language models (LLMs) to enhance reasoning and knowledge representation, employing techniques like multi-modal fusion and personalized low-rank adaptation to improve accuracy and efficiency. These advancements are crucial for improving user experience in various applications, from e-commerce and streaming services to personalized IoT device control, while also addressing privacy concerns inherent in data-driven recommendation systems.