A dynamic framework for maintaining customer profiles in e-commerce recommender systems

Recommender systems have been successfully applied to enhance the quality of service for customers, and more importantly, to increase the sale of products and services in e-commerce business. In order to provide effective recommendation results within an acceptable response time, a recommender system is required to have the scalability to handle a large customer population in real time. In this paper, we propose a new recommender system framework based on the incremental clustering algorithm in order to dynamically maintain the customer profiles. Using the incremental clustering technique, the dynamic changes in the number of customers and products purchased could be handled effectively. Experiments on real data sets showed that the proposed framework helps to reduce the recommendation time, while retaining accuracy.