Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods

Multi-Agent Negotiation in B2C E-Commerce Based on Data Mining Methods

Bireshwar Dass Mazumdar (Institute of Technology, Banaras Hindu University, India) and R. B. Mishra (Institute of Technology, Banaras Hindu University, India)
Copyright: © 2010 |Pages: 25
DOI: 10.4018/jiit.2010100104
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Abstract

The Multi agent system (MAS) model has been extensively used in the different tasks of e-commerce like customer relation management (CRM), negotiation and brokering. For the success of CRM, it is important to target the most profitable customers of a company. This paper presents a multi-attribute negotiation approach for negotiation between buyer and seller agents. The communication model and the algorithms for various actions involved in the negotiation process is described. The paper also proposes a multi-attribute based utility model, based on price, response-time, and quality. In support of this approach, a prototype system providing negotiation between buyer agents and seller agents is presented.
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Problem Description

From the perspective of customer orientation for customer relationship marketing (CRM), establishing and maintaining the best possible relationship with valuable buyer is a good way to survive in the competitive global market. The problem is firstly described by collecting information of 23 business and cognitive parameters of buyers and sellers agents.

The proposed model is orientation based profitable buyers and profitable seller categorization system based on data mining and agent technology that designs, executes (on-line, etc.) on business and cognitive parameters of buyers and sellers and conducts data mining process for the profitable buyers and sellers categorization . It has multi-agent based architecture and integration of data mining process into decision support system framework (Figure 1).

Figure 1.

Interaction between buyer and seller agent

There are five types of intelligent agents within the architecture:

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