Leveraging Artificial Intelligence to Drive Engagement and Conversions in Social Commerce Platforms

Tykea Khy

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 2, 1845-1854

Abstract: Integrating artificial intelligence (AI) in social commerce represents a transformative force reshaping how businesses engage with consumers and drive conversions in digital marketplaces. This comprehensive article examines AI's technical foundations, implementation strategies, and future directions within social commerce platforms. The article analyzes how sophisticated machine learning algorithms, computer vision technologies, conversational AI, and predictive analytics collectively enhance the shopping experience through personalization and engagement. It further investigates the critical frameworks underlying these technologies, including user profiling systems and dynamic content optimization engines that deliver tailored consumer experiences. As privacy concerns grow, implementing ethical AI practices, including federated learning, differential privacy, and explainable, becomes increasingly vital. Emerging technologies such as multimodal learning, edge AI, augmented reality commerce, and blockchain integration promise to further revolutionize the social commerce landscape, creating more intuitive, transparent, and effective shopping experiences that blur traditional boundaries between social interaction and commerce.

Keywords: Personalization frameworks; conversational AI; computer vision; privacy-preserving techniques; multimodal learning (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112548
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT25112548 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT25112548/CSEIT25112548 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i2:id:1245

DOI: 10.32628/CSEIT25112548

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma ().