國立虎尾科技大學 |

Unraveling Biases and Customer Heterogeneity in E-Commerce Recommendation Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Unraveling Biases and Customer Heterogeneity in E-Commerce Recommendation Systems./
Author:	Sharma, Sachin.
Description:	1 online resource (125 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Information technology. -
Online resource:	click for full text (PQDT)
ISBN:	9798382746586

Unraveling Biases and Customer Heterogeneity in E-Commerce Recommendation Systems.
Sharma, Sachin.

Unraveling Biases and Customer Heterogeneity in E-Commerce Recommendation Systems. - 1 online resource (125 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (D.B.A.)--University of Missouri - Saint Louis, 2024.

Includes bibliographical references

This research explores the biases present in AI algorithms within e-commerce recommendation systems, focusing on how these biases prioritize popular, sponsored, and private-label products over actual customer preferences. We extend the responsible AI discourse by critically examining these biases and their implications for fairness in e-commerce. To strengthen the current understanding of AI fairness in the fields of information systems and computer science, we aim to challenge the assumption that AI fairness is objective and the same for everyone. We examine how individual differences, such as equity sensitivity and exchange ideology, contribute to users' varied perceptions of AI fairness. Through a factorial design survey, we find that customers perceive popularity bias as less unfair than sponsored or private-label biases, indicating a possible preference for conformity or 'herd behavior.' Further, our findings support the influence of exchange ideology, as individuals with higher levels of this trait tend to view recommendation systems as more unfair. However, we did not find similar empirical support for equity sensitivity trait. Finally, we find that perceived fairness negatively influences distrust towards the recommendation systems. Our findings emphasize that addressing customer fairness perceptions is vital for mitigating distrust and enhancing the effectiveness of these systems. We conclude by discussing the theoretical contributions to the literature on AI and organizational justice and practical recommendations for improving the fairness of AI recommendation systems in e-commerce.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382746586Subjects--Topical Terms:

559429
Information technology.
Subjects--Index Terms:

Private-label biasesIndex Terms--Genre/Form:

554714
Electronic books.

Unraveling Biases and Customer Heterogeneity in E-Commerce Recommendation Systems.
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500 $a Advisor: Singh, Vivek K.
502 $a Thesis (D.B.A.)--University of Missouri - Saint Louis, 2024.
504 $a Includes bibliographical references
520 $a This research explores the biases present in AI algorithms within e-commerce recommendation systems, focusing on how these biases prioritize popular, sponsored, and private-label products over actual customer preferences. We extend the responsible AI discourse by critically examining these biases and their implications for fairness in e-commerce. To strengthen the current understanding of AI fairness in the fields of information systems and computer science, we aim to challenge the assumption that AI fairness is objective and the same for everyone. We examine how individual differences, such as equity sensitivity and exchange ideology, contribute to users' varied perceptions of AI fairness. Through a factorial design survey, we find that customers perceive popularity bias as less unfair than sponsored or private-label biases, indicating a possible preference for conformity or 'herd behavior.' Further, our findings support the influence of exchange ideology, as individuals with higher levels of this trait tend to view recommendation systems as more unfair. However, we did not find similar empirical support for equity sensitivity trait. Finally, we find that perceived fairness negatively influences distrust towards the recommendation systems. Our findings emphasize that addressing customer fairness perceptions is vital for mitigating distrust and enhancing the effectiveness of these systems. We conclude by discussing the theoretical contributions to the literature on AI and organizational justice and practical recommendations for improving the fairness of AI recommendation systems in e-commerce.
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