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AI-Driven Virtual Personal Shoppers

Divyasree D, Aparna Saju, Adil Sharaf, Abhirami S, Smitha C S

Abstract


In the era of digital transformation, the convergence of artificial intelligence (AI) and e-commerce has led to the emergence of AI-driven Virtual Personal Shoppers (VPS), revolutionizing the traditional retail landscape. VPS utilize advanced machine learning algorithms and natural language processing techniques to comprehend individual preferences, style inclinations, and purchasing behaviors. By leveraging vast datasets and consumer interaction histories, VPS offer personalized product recommendations and guidance, mirroring the tailored assistance provided by in-store personal shoppers. This abstract explores the multifaceted implications of AI-driven VPS on consumer engagement, brand loyalty, and the retail industry at large. It investigates the underlying technological frameworks, challenges pertaining to privacy and data security, as well as the potential socioeconomic ramifications of widespread VPS adoption. Furthermore, the abstract examines the dynamic interplay between AI-driven VPS and evolving consumer expectations, highlighting the need for continuous adaptation and innovation within the retail sector. Through a comprehensive analysis, this abstract elucidates the transformative potential of AI-driven VPS in shaping the future of retail, offering insights into the opportunities and considerations for stakeholders navigating this paradigm shift.


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References


M. F. Hashmi, B. K. K. Ashish, A. G. Keskar, N. D. Bokde and Z. W. Geem, "FashionFit: Analysis of Mapping 3D Pose and Neural Body Fit for Custom Virtual Try- On," in IEEE Access, vol. 8, pp. 91603-91615, 2020, doi: 10.1109/ACCESS.2020.2993574.J.

Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.

L. Yu, Y. Zhong and X. Wang, "Inpainting-Based Virtual Try-on Network for Selective Garment Transfer," in IEEE Access, vol. 7, pp. 134125-134136, 2019, doi: 10.1109/ACCESS.2019.2941378.

M. Alamdari, N. J. Navimipour, M. Hosseinzadeh, A. A. Safaei and A. Darwesh, "A Systematic Study on the Recommender Systems in the E-Commerce," in IEEE Access, vol. 8, pp. 115694-115716, 2020, doi: 10.1109/ACCESS.2020.3002803.

Maleki Shoja and N. Tabrizi, "Customer Reviews Analysis With Deep Neural Networks for E-Commerce Recommender Systems," in IEEE Access, vol. 7, pp. 119121- 119130, 2019, doi: 10.1109/ACCESS.2019.2937518.

G. Daniel, J. Cabot, L. Deruelle and M. Derras, "Xatkit: A Multimodal Low-Code Chatbot Development Framework," in IEEE Access, vol. 8, pp. 15332-15346, 2020, doi: 10.1109/ACCESS.2020.2966919.

T. Wang, X. Gu and J. Zhu, "A Flow-Based Generative Network for Photo-Realistic Virtual Try-on," in IEEE Access, vol. 10, pp. 40899-40909, 2022, doi: 10.1109/ACCESS.2022.3167509.

J. Yao and H. Zheng, "LC-VTON: Length Controllable Virtual Try-on Network," in IEEE Access, vol. 11, pp. 88451-88461, 2023, doi: 10.1109/ACCESS.2023.3306449.

K. Zheng, X. Yang, Y. Wang, Y. Wu and X. Zheng, "Collaborative filtering recommendation algorithm based on variational inference," in International Journal of Crowd Science, vol. 4, no. 1, pp. 31-44, March 2020, doi: 10.1108/IJCS-10-2019-0030.

H. Hwangbo, E. H. Kim, S. -H. Lee and Y. J. Jang, "Effects of 3D Virtual “Try-On” on Online Sales and Customers’ Purchasing Experiences," in IEEE Access, vol. 8, pp. 189479-189489, 2020, doi: 10.1109/ACCESS.2020.3023040.


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