Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/34199
Author(s): | Duwadi, S. Coutinho, C. |
Editor: | Nuno Mateus Coelho Manuela Cruz Cunha |
Date: | 2024 |
Title: | ChatGPT based recommendation system for retail shops |
Volume: | 237 |
Book title/volume: | Procedia Computer Science |
Pages: | 253 - 260 |
Event title: | 2023 International Conference on Industry Sciences and Computer Science Innovation, iSCSi 2023 |
Reference: | Duwadi, S., & Coutinho, C. (2024). ChatGPT based recommendation system for retail shops. In N. M.-Coelho, & M. C. Cunha (Eds.), Procedia Computer Science (pp. 253-260). Elsevier. https://doi.org/10.1016/j.procs.2024.05.103 |
ISSN: | 1877-0509 |
DOI (Digital Object Identifier): | 10.1016/j.procs.2024.05.103 |
Keywords: | Artificial Intelligence (AI) AI assistant Retail business E-commerce Personalized recommendation systems Machine learning ChatGPT |
Abstract: | The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Size | Format | |
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conferenceObject_103342.pdf | 881,88 kB | Adobe PDF | View/Open |
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