Activity Recommendation: Optimizing Life in the Long Term

Julian A. Ramos Rojas, Johana Rosas, Yilin Shen, Hongxia Jin, Anind K. Dey

Abstract: College students every day decide and plan how to best spend their time to balance academic, physical, and social goals under uncertainty. This process is likely suboptimal where long-term life satisfaction and success is not guaranteed, and poor decision-making may lead to longer-term problems like depression. To support everyday planning, we introduce activity recommendation, a novel method that combines artificial intelligence, machine learning, and a psychology-informed approach to automatically generate activity-recommendations that optimize long-term life satisfaction. We tested our method with an existing dataset and derived activity recommendations for depressed and non-depressed students. We evaluated the recommendations through interviews with college students who rated the suggestions positively. Our model can be optimized for different goals and domains and is easy to interpret. Our results demonstrate the feasibility of our approach and lay the groundwork towards implementing a live system.