Algorithmic Transparency and Consumer Trust: Ethical Reflection and Development Strategies of Personalized Recommendations in E-Commerce

DOI: https://doi.org/10.62517/jmsd.202612134

Author(s)

Lan Yao

Affiliation(s)

Department of Digital Business, Qingdao Huanghai University, Qingdao, China

Abstract

With the increasing penetration of artificial intelligence technologies, personalized recommendation systems have become a core instrument for e-commerce platforms to enhance user engagement and improve conversion rates. However, the consumer trust crisis induced by the “black box” nature of algorithms has grown increasingly prominent. Striking a balance between improving recommendation accuracy and ensuring algorithmic transparency while safeguarding consumer rights has thus emerged as a central concern for both academia and industry. This paper synthesizes existing literature on algorithmic transparency and, in response to the practical challenges currently faced—namely, lack of recommendation fairness, information cocoons, privacy violations, and ambiguous attribution of responsibility—proposes innovative strategies including hierarchical transparency design, user control empowerment, and institutional collaborative governance. The study aims to provide both theoretical support and practical guidance for the development of a trustworthy e-commerce ecosystem.

Keywords

Algorithmic Transparency; Consumer Trust; Personalized Recommendation; E-Commerce Ethics

References

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