Industrial Recommender System

Industrial recommender systems aim to provide personalized recommendations at scale, optimizing user engagement and business metrics like click-through rates and conversions. Current research emphasizes improving model robustness and efficiency through techniques like self-supervised multi-task learning, addressing biases (e.g., popularity bias), and incorporating multimodal data and large language models to enhance representation learning and knowledge transfer across domains. These advancements are crucial for enhancing the performance and scalability of real-world recommendation systems across diverse platforms, impacting user experience and business outcomes.