Authors : Sagar D. Patil; Dr. Manisha Bharati

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/3h2zt23m

Scribd : https://tinyurl.com/ye2x75hs

DOI : https://doi.org/10.38124/ijisrt/26May1751

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Fixed pricing in e-commerce platforms creates a significant gap compared to the interactive bargaining experience customers enjoy in physical retail environments. This paper presents BargainBot, a deployed full-stack AI chatbot that enables real-time, free-form price negotiation on e-commerce platforms. The proposed system integrates three core components:  A PyTorch Recurrent Neural Network (RNN) trained on a custom multilingual intent dataset spanning English, Hindi, and Marathi, achieving approximately 91% classification accuracy across 15 negotiation intent classes.  A rule-based negotiation engine that enforces per-product seller margin constraints while generating contextually appropriate counter-offers.  A sentiment-aware response layer that detects buyer tone and adjusts negotiation strategy accordingly. Unlike prior work that relies on fixed command keywords or scripted menus, BargainBot accepts fully free-form natural-language input, disambiguates discount-amount offers from target-price offers, and maintains offer state across multiple conversation turns. The system is implemented as a decoupled React.js frontend communicating with a Django REST backend, supporting 37 products across six categories. Experimental evaluation demonstrates a negotiation success rate of 84%, intent classification accuracy of ~91%, and 100% seller margin protection across all tested scenarios.

Keywords : Price Negotiation, E-Commerce Chatbot, Recurrent Neural Network, Intent Classification, Natural Language Processing, Multi-Turn Dialogue, Rule-Based Reasoning, Sentiment Analysis, Dynamic Pricing.

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