BargainBot: AI-Driven Price Negotiation for E-Commerce: A Review

  • Unique Paper ID: 193584
  • PageNo: 2082-2083
  • Abstract:
  • This paper introduces BargainBot, an AI chatbot that enables real-time price negotiation in e-commerce. It integrates Natural Language Processing (NLP), Machine Learning (ML), and Reinforcement Learning (RL) to conduct multi-turn bargaining dialogues. The system aims to increase customer satisfaction, reduce cart abandonment, and protect seller profitability within predefined margins.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{193584,
        author = {Sagar Patil and Rashmi Dharmadhikari and Manisha Bharti},
        title = {BargainBot: AI-Driven Price Negotiation for E-Commerce: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2082-2083},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193584},
        abstract = {This paper introduces BargainBot, an AI chatbot that enables real-time price negotiation in e-commerce. It integrates Natural Language Processing (NLP), Machine Learning (ML), and Reinforcement Learning (RL) to conduct multi-turn bargaining dialogues. The system aims to increase customer satisfaction, reduce cart abandonment, and protect seller profitability within predefined margins.},
        keywords = {E-Commerce, Negotiation Chatbot, Dynamic Pricing, NLP, Machine Learning, Reinforcement Learning.},
        month = {March},
        }

Cite This Article

Patil, S., & Dharmadhikari, R., & Bharti, M. (2026). BargainBot: AI-Driven Price Negotiation for E-Commerce: A Review. International Journal of Innovative Research in Technology (IJIRT). https://doi.org/doi.org/10.64643/IJIRTV12I10-193584-459

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