Personalized Ranking

Personalized ranking aims to order items (e.g., products, emails, news articles) for individual users based on predicted preferences, optimizing user experience and engagement. Current research emphasizes improving ranking accuracy through advanced model architectures like graph neural networks and large language models, often incorporating user feedback (explicit ratings or implicit interactions) and contextual information (e.g., time, user history). This field is crucial for enhancing recommender systems and other applications requiring personalized information filtering, with ongoing efforts focused on addressing biases, improving model interpretability, and ensuring fairness in rankings.