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

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Title: Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model.
Authors: Lin, Feng1 (AUTHOR), Qian, Xiaoning2 (AUTHOR), Mortazavi, Bobak3 (AUTHOR), Wang, Zhangyang4 (AUTHOR), Huang, Shuai1 (AUTHOR) shuai.huang.ie@gmail.com, Chen, Cynthia5 (AUTHOR)
Source: IISE Transactions. Dec2024, Vol. 56 Issue 12, p1307-1320. 14p.
Subjects: Data corruption, Mobile apps, Prediction models, Algorithms, Success
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. [ABSTRACT FROM AUTHOR]
Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model.
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  Data: <searchLink fieldCode="JN" term="%22IISE+Transactions%22">IISE Transactions</searchLink>. Dec2024, Vol. 56 Issue 12, p1307-1320. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Data+corruption%22">Data corruption</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+apps%22">Mobile apps</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Success%22">Success</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/24725854.2023.2279080
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        Text: English
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              Text: Dec2024
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