Authors: Teemu Mutanen, Sami Nousiainen and He Liang
We present several recommendation approaches how discount coupon service could be personalized in order to cut down customer’s usage time. The Velo system is already implemented coupon service system in China with millions of users. The proposed approach makes use of customer and dispenser meta data together with previous user coupon prints in order to predict upcoming prints for a customer. The implementation of the service, data collection, and how the implementation affects the customers’ behavior all have an effect on the accuracy of the printed coupons. The analysis of customer characteristics help the service to present relevant coupons to a customer in order both to raise customer’s satisfaction and to aid customers on finding relevant coupons faster. The results show that item recommendation systems provide valuable benefits for coupon service.
Keywords: Personalization; Discount Coupon; Recommendation Systems; User Modeling