Online Recommender System

Online recommender systems aim to predict and personalize user preferences, dynamically suggesting relevant items (products, ads, content) to maximize user engagement and platform revenue. Current research emphasizes improving recommendation accuracy and explainability through techniques like incorporating large language models for generating explanations and simulating user behavior to train reinforcement learning models, as well as addressing challenges such as cold-start problems and evolving user interests via graph-based embeddings and meta-learning approaches. These advancements are crucial for enhancing user experience, optimizing platform performance, and providing valuable insights into user behavior and decision-making processes.