LLM for Retail Business (Optimizing Clothing Sales with AI)
DOI:
https://doi.org/10.32628/IJSRSET24115108Keywords:
Natural Language Processing, Large Language Model, Retail Industry, Google Palm, StreamlitAbstract
This research paper presents an end-to- end implementation of a chatbot system tailored for the retail industry, utilizing a large language model (LLM). The chatbot is designed to assist employees of retail stores, such as clothing outlets, by providing real-time access to critical business data, including inventory levels, sales metrics, and profit margins. The solution aims to streamline decision- making processes, enhance operational efficiency, and improve information accessibility by reducing dependency on manual data retrieval. This approach leverages advanced natural language processing to simplify the interface between business systems and employees, ensuring accurate and timely responses to queries.
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Niksa Alfirevic, Daniela Garbin and Pranicevic, “Custom - Trained Large Language Models as Open Educational Resource” published on MDPI, June 2024.
M.F. Mridha & Talha Bin Sarwar, “A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM” published on Scientific reports, April 2024.
B. C. Surve, B. Nemade, and V. Kaul, "Nano-electronic devices with machine learning capabilities," ICTACT Journal on Microelectronics, vol. 9, no. 3, pp. 1601-1606, Oct. 2023, doi: 10.21917/ijme.2023.0277.
G. Khandelwal, B. Nemade, N. Badhe, D. Mali, K. Gaikwad, and N. Ansari, "Designing and Developing novel methods for Enhancing the Accuracy of Water Quality Prediction for Aquaponic Farming," Advances in Nonlinear Variational Inequalities, vol. 27, no. 3, pp. 302-316, Aug. 2024, ISSN: 1092-910X.
B. Nemade, S. S. Alegavi, N. B. Badhe, and A. Desai, “Enhancing information security in multimedia streams through logic learning machine assisted moth-flame optimization,” ICTACT Journal of Communication Technology, vol. 14, no. 3, 2023.
Stefano Filippi and Barbara Motyl, “Large Language Models (LLMs) in Engineering Education: A Systematic Review and Suggestions for Practical Adoption” published on MDPI, June 2024. DOI: https://doi.org/10.3390/info15060345
Rajvardhan Patil and Venkat Gudivada, “A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)” published on MDPI journal, March 2024. DOI: https://doi.org/10.20944/preprints202402.0357.v1
Olamilekan Shobayo, Swethika Sasikumar Sandhya Makkar, “Customer Sentiments in Product Reviews: A Comparative Study with GooglePaLM” published on MDPI journal June 2024. DOI: https://doi.org/10.3390/analytics3020014
Mohaimenul khan, Saddam Mukta, “Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges”, published on IEEE, Jun 2024.
Chang Victor, Hall Karl, “Prediction of Customer Churn Behavior in the Telecommunication Industry Using Machine Learning Models”, published on Scientific reports, June 2024. DOI: https://doi.org/10.3390/a17060231
Saadat Izadi, Mohmad Forouzanfar, “Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots”, published on published on MDPI journal, June 2024. DOI: https://doi.org/10.3390/ai5020041
Alfirevic Niksa, Mabic Mirela, “Large Language Models for Business: Current State and Future Directions”, published on Scientific reports, April 2023.
Hong Zhang, Haijian Shao, “Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey”, published on Tech Science Press, Dec 2023.
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