Skip to main content

Cloud-Based Smart Retail System Using AI-Driven Recommendations

Mallikarjun Bellundagi

Abstract

The rapid advancement of cloud computing and artificial intelligence (AI) has significantly transformed the retail industry. This paper presents a cloud-based smart retail system that leverages AI-driven recommendation techniques to enhance customer experience, optimize inventory management, and improve sales performance. The proposed system integrates machine learning algorithms, real-time analytics, and scalable cloud infrastructure to deliver personalized product recommendations. The study adopts a conceptual-analytical methodology supported by literature synthesis and system design modeling. Results indicate that AI-powered retail systems improve customer engagement, increase conversion rates, and enable data-driven decision-making. The proposed framework contributes to the development of intelligent, scalable, and adaptive retail ecosystems.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 4 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0804005

Pages

14601-14609

Published

July 9, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mallikarjun Bellundagi (%2025). Cloud-Based Smart Retail System Using AI-Driven Recommendations. International Journal of Science, Research and Technology , Vol. 8 No. 4 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14601-14609. https://doi.org/10.15662/IJSRAT.2025.0804005

References

[1] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.
[2] F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook. New York, NY, USA: Springer, 2011.
[3] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, Jan. 2003.
[4] P. Resnick and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997.
[5] M. J. Pazzani and D. Billsus, “Content-based recommendation systems,” in The Adaptive Web, Berlin, Germany: Springer, 2007, pp. 325–341.
[6] R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331–370, 2002.
[7] M. Armbrust et al., “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010.
[8] Q. Zhang and M. Chen, “Cloud computing: State-of-the-art and research challenges,” Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7–18, 2010.
[9] A. Smith, “Personalization in retail: Trends and challenges,” Journal of Retail Analytics, vol. 14, no. 2, pp. 45–52, 2018.
[10] V. Kumar, “Customer engagement strategies in digital retail,” Journal of Marketing Research, vol. 56, no. 3, pp. 1–15, 2019.
[11] M. A. Waller and S. E. Fawcett, “Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management,” Journal of Business Logistics, vol. 34, no. 2, pp. 77–84, 2013.
[12] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th ed. Thousand Oaks, CA, USA: Sage, 2014.
[13] P. Mell and T. Grance, “The NIST definition of cloud computing,” NIST Special Publication 800-145, 2011.
[14] R. Buyya, C. Vecchiola, and S. T. Selvi, Mastering Cloud Computing. New York, NY, USA: McGraw-Hill, 2013.
[15] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, pp. 30–37, 2009.
[16] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
[17] Amazon Web Services, “AWS Documentation,” 2023. [Online]. Available: https://aws.amazon.com/documentation/
[18] Microsoft, “Azure Documentation,” 2023. [Online]. Available: https://learn.microsoft.com/azure/
[19] McKinsey & Company, “The value of personalization in retail,” 2021. [Online]. Available: https://www.mckinsey.com/
[20] European Union, “General Data Protection Regulation (GDPR),” 2018.
[21] L. Floridi, The Ethics of Artificial Intelligence. Oxford, U.K.: Oxford Univ. Press, 2019.
[22] J. Gubbi et al., “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.
[23] M. H. Huang and R. T. Rust, “Artificial intelligence in retailing: Framework and applications,” Journal of Retailing, vol. 96, no. 2, pp. 209–220, 2020.
[24] H. Chen, R. H. Chiang, and V. C. Storey, “Business intelligence and analytics: From big data to big impact,” MIS Quarterly, vol. 36, no. 4, pp. 1165–1188, 2012.
[25] T. H. Davenport and R. Ronanki, “Artificial intelligence for the real world,” Harvard Business Review, vol. 96, no. 1, pp. 108–116, 2018.