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@article{175113,
        author = {Miss. Bhagyashree Sanjay Metre and Dr. V. S. Gulhane and Prof. H. N. Datir},
        title = {PRICE NEGOTIATION CHATBOT USING AI-BASED ENSEMBLE MACHINE LEARNING TECHNIQUES FOR E-COMMERCE WEBSITE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1682-1687},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175113},
        abstract = {E-commerce platforms have revolutionized retail by providing users with convenient access to a vast range of products. However, traditional fixed-price models often lack flexibility, leading to missed opportunities for both buyers and sellers. This research presents an Intelligent Negotiation Bot that leverages machine learning techniques to enable dynamic price negotiation in e-commerce transactions. The proposed system integrates the OpenAI API for chatbot-based interactions and Random Forest for price prediction, ensuring an optimized pricing strategy tailored to individual users.
The negotiation bot considers multiple factors such as user profile (new, old, or frequent buyer), purchasing behavior (bulk vs. single-item purchases), product demand analysis, and competitor pricing to generate personalized price recommendations. Natural Language Processing (NLP) is employed to analyze user preferences, while sentiment analysis on product ratings and reviews further refines price adjustments. The system consists of several modules, including an Admin Panel for product and order management, User Management, Product Catalog, AI-driven Price Negotiation, and Purchase Processing.
The workflow of the negotiation bot involves real-time interaction with users, where price recommendations are dynamically adjusted based on historical sales data and market demand patterns. Machine learning models, particularly Random Forest algorithms, are utilized to predict the optimal pricing strategy by analyzing past sales, seasonal trends, and demand fluctuations. The integration of an AI-driven chatbot ensures a seamless and human-like negotiation experience, enhancing user engagement and increasing conversion rates.
The proposed system bridges the gap between fixed pricing models and customer-specific dynamic pricing, fostering a more personalized shopping experience. By combining machine learning-driven price prediction, demand analysis, and AI-based negotiation, this research provides a novel approach to automated e-commerce price negotiation, ultimately benefiting both consumers and online retailers.},
        keywords = {E-commerce, Intelligent Negotiation, Machine Learning, Price Prediction, Random Forest, OpenAI API, AI Chatbot, Demand Analysis, Personalized Pricing.},
        month = {April},
        }