A Learning Framework for Personalized Random Utility Maximization (RUM) Modeling of User Behavior.

ABSTRACT: 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 collaborati

SUBMITTER: Feng J 

PROVIDER: S-EPMC9635458 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature