Group recommender systems generate a set of recommendations that will satisfy a group of customers with potentially competing purchase interests. This paper proposes a research and operational model which effectively enhances Group Recommender Model to boost the customer purchases. For this purpose, it uses the communication and collaboration of two major sources namely Mobile Money Operator and Outlet. MMO proactively monitors the spending pattern of the customers who make purchases using their mobile money. Outlet performs customer segmentation based on RFM (Recency, Frequency and Monetary) score after which a Recursive Cluster Elimination is performed that eliminates customers within the targeted segment. Recursive Frequent Item set Mining and Recursive Market Basket Analysis are performed for the rest of customers in the targeted segment. From the obtained results, the product preferences of the remaining customers in the segment are identified based on which offers are formulated and recommended for the entire segment. It is then communicated to the MMO that intimates these offers to the potential customers among the segment. This model results in boosting customer purchases, expanding customer base and effects in the profitability of the combined source.
M. Saravanan, Prasad, G., Jagadeesan, M., Raghu Raman, and V Smrithi Rekha, “Group Recommender Model for Boosting and Optimizing Customer Purchases”, in Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on, 2012.