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Modeling user interaction with app-based reward system: A graphical model approach integrated with max-margin

Jingshuo Feng1, Shuai Huang1, Cynthia Chen2

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Summary
This summary is machine-generated.

Smart apps can change user behavior, but traditional models fail. We propose a new graphical model, the Latent Decision Threshold (LDT) model, to better understand and predict user actions in response to app interactions and rewards.

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App-user interaction dataGraphical modelMax-margin learningPersonalized behavior modelTravel behavior

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Area of Science:

  • Behavioral Science
  • Computer Science
  • Machine Learning

Background:

  • Smart applications increasingly use personalized rewards to influence user behavior.
  • Accurate user behavior modeling is crucial for app effectiveness.
  • Existing statistical choice models, like Random Utility Maximization (RUM), assume data independence from user actions, which is often violated in interactive app environments.

Purpose of the Study:

  • To address the limitations of existing models in capturing user behavior influenced by smart apps.
  • To propose a novel graphical model for user behavior that accounts for the interactive nature of app-user dynamics.
  • To develop an effective computational strategy for learning this new model.

Main Methods:

  • User behavior is modeled as a graphical model.
  • The proposed graphical model is named the Latent Decision Threshold (LDT) model.
  • A max-margin formulation is developed as a computational strategy to overcome learning challenges associated with the LDT model.

Main Results:

  • Demonstrated the limitations of traditional choice models (e.g., RUM) in interactive app contexts.
  • Introduced the Latent Decision Threshold (LDT) model, a graphical approach to user behavior.
  • Developed and applied a max-margin learning strategy for the LDT model.

Conclusions:

  • Standard choice models are insufficient for understanding user behavior in interactive smart applications.
  • The Latent Decision Threshold (LDT) model offers a more suitable framework for modeling behavior influenced by app interactions.
  • The proposed max-margin computational strategy enables effective learning of the LDT model, advancing the design of behavior-changing applications.