Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model

Abstract

Recent years have witnessed the emergence of many new mobile apps and user-centered systems that interact with users by offering choices with rewards. These applications have been promising to address challenging societal problems such as congestion in transportation and behavior changes for healthier lifestyles. Considerable research efforts have been devoted to model the user behaviors in these new applications. However, as real-world user data is often prone to data corruptions, the success of these models hinges on a robust learning method. Building on the recently proposed Latent Decision Threshold model, this article shows that, among the existing robust learning frameworks, the L0-norm-based framework can outperform other state-of-the-art methods in terms of prediction accuracy and model estimation. And based on the L0-norm framework, we further develop a user screening algorithm to identify potential bad actors.

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