Personalised e-Commerce Assistant

Registration ID: IJNRD_208142

Published ID: IJNRD2311126

DOI: Click Here to Get

Tom Shaju , Arpan Dixit , Ritam Basu

Machine Learning; E-Commerce; Personalised; Collaborative Filtering;GPT

Shopping needs can be expressed intuitively by users thanks to PEA's deep learning architecture, which is trained using extensive customer data and product information. Enhancing customer satisfaction, PEA employs collaborative filtering and content-based recommendation algorithms to precisely align each product suggestion with a user's unique tastes and preferences. In order to enhance the shopping experience, PEA continuously adapts and evolves using a learning mechanism that reflects the changing preferences of users. Maintaining customer trust in the platform, PEA uses cutting-edge encryption and anonymization techniques to safeguard user information, thus ensuring data privacy. The proposed system consists of two primary components: a natural language processing (NLP) module powered by GPT and a recommendation engine based on collaborative filtering. The NLP module enables seamless and intuitive interactions between users and the e-commerce platform. Users can ask questions, seek product advice, or provide preferences in natural language, and GPT interprets and responds to their queries intelligently.

Published Paper   E-Certificate

Tom Shaju, Arpan Dixit, & Ritam Basu (November-2023). Personalised e-Commerce Assistant. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), b211-b216. https://ijnrd.org/papers/IJNRD2311126.pdf

Volume 8 Issue 11, November-2023

Pages : b211-b216

Paper Reg. ID: IJNRD_208142

Published Paper Id: IJNRD2311126

Downloads: 000121975

Research Area: Computer Science & Technology 

Country: Chennai, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311126.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311126

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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