AI-Powered Recommendation Systems: Exploring their Impact on Customer-Business Interaction

AI-powered recommendation systems have revolutionized customer-business interactions by leveraging machine learning to deliver personalized experiences. This study investigates their multifaceted impact across sectors like e- commerce, streaming services, and social media. Through a mixed-methods approach—including a literature review, case studies (Netflix, Amazon), and a 15-participant survey—the research highlights how these systems enhance engagement, satisfaction, and revenue. Ethical challenges such as privacy concerns, algorithmic bias, and filter bubbles are critically analyzed. Findings reveal that while AI recommendations drive loyalty and discovery, addressing transparency and user control remains vital for sustainable adoption. The study concludes with actionable insights for businesses and policymakers to balance innovation with ethical responsibility.

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Publication Details

Published
2025-04-25
DOI
https://doi.org/10.38124/ijisrt/25apr623
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Recommendation Systems: Exploring their Impact on Customer-Business Interaction

Fatma SBIAI
Recommender Systems and Techniques
article

AI-Powered Recommendation Systems: Exploring their Impact on Customer-Business Interaction

Fatma SBIAI
article en

Abstract

AI-powered recommendation systems have revolutionized customer-business interactions by leveraging machine learning to deliver personalized experiences. This study investigates their multifaceted impact across sectors like e- commerce, streaming services, and social media. Through a mixed-methods approach—including a literature review, case studies (Netflix, Amazon), and a 15-participant survey—the research highlights how these systems enhance engagement, satisfaction, and revenue. Ethical challenges such as privacy concerns, algorithmic bias, and filter bubbles are critically analyzed. Findings reveal that while AI recommendations drive loyalty and discovery, addressing transparency and user control remains vital for sustainable adoption. The study concludes with actionable insights for businesses and policymakers to balance innovation with ethical responsibility.

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Recommender Systems and Techniques
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