Smart shopper: an agent-based web-mining approach to Internet shopping

With the rapid development of e-commerce, there is an immediate need for an autonomous and robust system that can offer decision-making support for customers who are looking for the cheapest, the most familiar, or the best-quality product. This paper presents an agent-based web-mining approach to Internet shopping. In contrast to the traditional multiagent systems, which use rule-based or case-based process flows to coordinate communications for system automation, we propose a fuzzy neural network to tackle the uncertainties in practical shopping activities, such as consumer preferences, product specification, product selection, price negotiation, purchase, delivery, after-sales service and evaluation. The fuzzy neural network provides an automatic and autonomous product classification and selection scheme to support fuzzy decision making by integrating fuzzy logic technology and the back propagation feed forward neural network. In addition, a new visual data model is introduced to overcome the limitations of the current web browsers that lack flexibility for customers to view products from different perspectives. Such a model also extends the conventional data warehouse schema to deal with intensive data volumes and complex transformations with a high degree of flexibility for multiperspective visualization and morphing capability in an interactive environment. Furthermore, an agent development tool named “Aglet” is used as a programming framework for system implementation. The integration of dynamic object visualization, interactive user interface and data mining decision support provides an effective technique to close the gap between the “real world” and the “cyber world” from a business perspective. The experimental results demonstrate the feasibility of the proposed approach for web-based business transactions.