Mining the profitability of customers and making right recommendations
Customers varying in profitability are not news. However, segmenting customers in terms of their profit contribution remains an underutilized approach in many recommender systems (RS). In this thesis, based on our previous research of customer segmentation, we have extended our work by proposing a hybrid recommendation system which uses multi-agent system, collaborative filtering together to serve the customers with high profitability, while using simplified top-N to generate high efficient recommendations for the lower profitability customers. Also, agent-based recommendation strategy selection architecture has been proposed and examined in our research.