LAUSR

Understanding user behavior is crucial for the success of many emerging applications that aim to provide personalized services for target users, such as many patient-centered health apps and transportation apps. Models based on the random utility maximization (RUM) theory are widely used in learning and understanding behavioral preferences on the population level but find difficult to estimate individuals’ preferences, particularly when individuals’ data are limited and fragmented. To address this problem, our framework builds on the concepts such as canonical structure and membership vectors invented in recent works on collaborative learning and is suitable for modeling heterogeneous population with insufficient data from each individual. We further propose an extension of the collaborative learning framework using pairwise-fusion regularization as a knowledge discovery tool for real-world applications where the canonical structure is uneven, e.g., some canonical models may only represent minor subpopulations. Computationally competent algorithms are developed to solve the corresponding optimization challenges. Extensive simulation studies and a real-world application in smart transportation demand management (TDM) show the effectiveness of our proposed methods. Note to Practitioners—The proposed methods in this article can learn a distinct behavior model for each user and understand his/her own preferences, even when each of the user’s data is limited. With personalized models, it will help nowadays’ personalized service apps to understand, explain, and change user behavior in a more targeted and efficient way. The utility of the method is illustrated by an application on a real-world transportation demand management problem where personalized incentives are assigned to users to change their travel behavior.