In recent years, there has been a rapid growth of smart apps that could interact with users and implement personalized rewards to coordinate and change user behavior. Understanding user behavior is an enabling factor for the success of these promising apps. However, existing statistical models for modeling user behavior encounter limitations. Choice models based on Random Utility Maximization (RUM) commonly assume that the data collection is independent with the human behavior. However, when users interact with the apps, the real potential and also the real challenge for modeling user behavior is that the apps not merely are data collection tools, but also change users’ behaviors. In this work, we model the user behavior as a graphical model, examine our hypothesis that existing choice models are not suitable, and develop an interesting computational strategy using max-margin formulation to overcome the learning challenge of the our proposed graphical model that is named the Latent Decision Threshold (LDT) model.

近年来，可以与用户交互并实施个性化奖励以协调和改变用户行为的智能应用程序迅速增长。了解用户行为是这些有前途的应用程序成功的促成因素。然而，用于建模用户行为的现有统计模型遇到了限制。基于随机效用最大化 (RUM) 的选择模型通常假设数据收集与人类行为无关。然而，当用户与应用程序交互时，对用户行为建模的真正潜力和挑战在于，应用程序不仅是数据收集工具，还可以改变用户的行为。在这项工作中，我们将用户行为建模为图形模型，检查我们现有的选择模型不合适的假设，