PCFinder: an intelligent product recommendation agent for e-commerce

There are many e-commerce applications on the Web. A common shortcoming is the lack of customer service and marketing analysis tools in most e-commerce web sites. In order to overcome this problem, we have constructed an intelligent agent based on Case-Based Reasoning (CBR) and collaborative filtering, which we have included in our product recommendation system, called PCFinder. This system was four main characteristics. The first is applying novel methodologies based on CBR to an e-commerce application. We propose a heuristic to represent an Order-Based Similarity Measure, together with the method of weight modification and adaptation. The second is applying CBR and collaborative filtering techniques to make our intelligent agent more efficient and effective. We also apply clustering analysis techniques to assist our intelligent agent for grouping the customers according to their long-term profiles in order to analyze the user profiles (external attributes) and provide some suggestions of the items (internal attributes) of the product. The third is introducing a method for constructing product recommendation systems: from architecture to methodologies and from applied technologies to implementations. The last is providing a graphic-building wizard based on clustering analysis of the past purchasing history to the management staff for analyzing the marketing tendencies.