AI-Powered Personalization in Mobile Commerce Applications

Authors

Dr Deepak Mathur

Computer Science, Lachoo Memorial College of Science and Technology, Jodhpur (Rajasthan)

Article Information

DOI: 10.51584/IJRIAS.2025.10120066

Subject Category: Computer Science

Volume/Issue: 10/12 | Page No: 796-800

Publication Timeline

Submitted: 2025-12-25

Accepted: 2025-12-30

Published: 2026-01-16

Abstract

Mobile commerce (m-commerce) has significantly transformed consumer purchasing behavior by offering convenient and seamless shopping experiences through smartphones and tablets. As e-commerce platforms continue to expand rapidly, personalization has emerged as a critical factor for businesses aiming to boost user engagement, satisfaction, and customer loyalty. Artificial Intelligence (AI) serves as a driving force behind this personalization, utilizing machine learning algorithms, natural language processing, and predictive analytics to tailor shopping experiences to individual preferences. This paper examines the role of AI-powered personalization in mobile commerce applications, highlighting its advantages, challenges, and future opportunities.

Keywords

Mobile commerce has become a vital component of the digital economy

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References

1. ResearchGate. (2023). AI-Powered Personalization: Revolutionizing Mobile Commerce for Enhanced Customer Experiences. Retrieved from https://www.researchgate.net/publication/385042377_AI-Powered_Personalization_Revolutionizing_Mobile_Commerce_for_Enhanced_Customer_Experiences [Google Scholar] [Crossref]

2. ResearchGate. (2023). AI-Driven Personalization in E-Commerce. Retrieved from https://www.researchgate.net/publication/389626209_AI-DRIVEN_PERSONALIZATION_IN_E-COMMERCE [Google Scholar] [Crossref]

3. ResearchGate. (2023). E-commerce and Consumer Behavior: A Review of AI-Powered Personalization and Market Trends. Retrieved from https://www.researchgate.net/publication/379429755_E-commerce_and_consumer_behavior_A_review_of_AI-powered_personalization_and_market_trends [Google Scholar] [Crossref]

4. arXiv. (2022). An Empirical Study of AI Techniques in Mobile Applications. Retrieved from https://arxiv.org/abs/2212.01635 [Google Scholar] [Crossref]

5. arXiv. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. Retrieved from https://arxiv.org/abs/2403.19345 [Google Scholar] [Crossref]

6. arXiv. (2023). Consumer Acceptance of the Use of Artificial Intelligence in Online Shopping: Evidence from Hungary. Retrieved from https://arxiv.org/abs/2301.01277 [Google Scholar] [Crossref]

7. arXiv. (2023). When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities. Retrieved from https://arxiv.org/abs/2307.16376 [Google Scholar] [Crossref]

8. Business Insider. (2025). Amazon's AI Shopping Assistant 'Rufus' Projected to Generate $700 Million in 2025. Retrieved from https://www.businessinsider.com/amazon-predicts-700-million-potential-gain-ai-assistant-rufus-2025-4 [Google Scholar] [Crossref]

9. ScienceDirect. (2025). AI-Powered Personalized Advertising and Purchase Intention in E-Commerce. Retrieved from https://www.sciencedirect.com/science/article/pii/S2199853125001155 [Google Scholar] [Crossref]

10. MDPI. (2025). The Impact of AI-Personalized Recommendations on Clicking Behavior in E-Commerce. Retrieved from https://www.mdpi.com/0718-1876/20/1/21 [Google Scholar] [Crossref]

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