An Application of association rule learning in recommender systems for e-Commerce and its effect on marketing

Abstract

High annual customer churn rates and low customer attractions caused by poor marketing recommendations inhibit enterprises from making as much profit as they should. The purpose of this research was to derive a more optimized association rule learning algorithm that can be used in a web-based recommender system for small-scale enterprises. The method used was a case study approach on a small-scale enterprise called Makewa Hardware located in Ruiru, Kenya. Having access to the enterprise supported the use of the agile methodology, more specifically, extreme programming in the development of the system that applied the algorithm. A sample of training data consisting of transactions made in the past was obtained from the enterprise in order to create the machine learning aspects of the algorithm. The results howed that the derived association rule learning algorithm was able to learn and generate its own frequent-item-set and use this to give appropriate recommendations to customers. The results revealed the system’s ability to make more accurate recommendations.This was based on the pattern of purchases made from the hardware store by various customers. The recommendations were given on a weekly basis. The implication of the results on the subjects showed that more business owners are open to having intelligent systems help make and predict their sales. The findings can be applied not only in hardware stores but also in other retail stores. Future research can ensure that a normal dataset can be transformed into a market basket without it losing important information.