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

Feng Lin, Xiaoning Qian, Bobak Mortazavi, Zhangyang Wang, Shuai Huang and Cynthia Chen

IISE Transactions, 2024, vol. 56, issue 12, 1307-1320

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.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24725854.2023.2279080 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:56:y:2024:i:12:p:1307-1320

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/24725854.2023.2279080

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().