Building personalized recommendation system in e-commerce using association rule-based mining and classification

Due to the convenience of Internet, people can search for whatever information they need and buy whatever they want on the web. In the age of E-Commerce, it is difficult to provide support for customers to find the most valuable products that match their heterogeneous needs. Traditional approaches to this so-called personalization problem adopt predefined formats to describe the customer requirements. This always leads to distortion in eliciting requirement information and thus inaccurate recommendations. In this paper, we propose a personalized recommendation system using association rule mining and classification in e-commerce. Customer requirements are extracted from text documents and transformed into a set of significant phrases. Allowing the transformed transaction records, a set of association rule are mined from database using Apriori algorithm. CBA-CB algorithm is applied to produce the best rules out of the whole set of rules. The best classifiers are then generated after the test and validation of those rules, aimed to predict the item labels for new customer requirements and thus assigns the corresponding class labels to the customer. The system analysis and design of the proposed recommendation system as well as the implementation of prototype are also presented.