Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
Researchers have developed a model to optimize the interaction between users and AI-driven recommendation systems. The model considers the costs associated with user communication and the size of the recommendation set presented by the AI. It aims to maximize user utility by balancing the precision of user preference messages with the number of recommendations provided, particularly in high-dimensional product spaces. AI
IMPACT This research could lead to more efficient and user-friendly AI recommendation systems by optimizing the balance between information exchange and search costs.