Intelligent Automated Negotiation System in Business to Consumer E-Commerce

Abstract

E-Commerce is one of the world's most fast-paced industries where the significant aspect of these industries is that they are lacking Customer-Retailer Interaction. Due to the conventional human psychology of bargaining, a product with a lower price is still popular, and some of the products in this sector lack this kind of bargaining, which would be a cause for some of the products. With the advancement of machine learning, automated and Intelligent Agent negotiating system has become a prominent tool in E-Commerce. This paper presents a negotiation technique for establishing a mutually acceptable agreement between the negotiation system which represents supplier and customers, built using Minimum Profit Algorithm designed as per seller requirements and trained on UCI machine learning repository's online retailer dataset using XG Boost regressor for intelligence. This system outperforms the traditional way of negotiation and the model was able to achieve an accuracy of 91.53 percent.