Personal-discount sensitivity prediction for mobile coupon conversion optimization

The high adoption of smart mobile devices among consumers provides an opportunity for e-commerce retailers to increase their sales by recommending consumers with real time, personalized coupons that take into account the specific contextual situation of the consumer. Although context-aware recommender systems (CARS) have been widely analyzed, personalized pricing or discount optimization in recommender systems to improve recommendations' accuracy and commercial KPIs has hardly been researched. This article studies how to model user-item personalized discount sensitivity and incorporate it into a real time contextual recommender system in such a way that it can be integrated into a commercial service. We propose a novel approach for modeling context-aware user-item personalized discount sensitivity in a sparse data scenario and present a new CARS algorithm that combines coclustering and random forest classification (CBRF) to incorporate the personalized discount sensitivity. We conducted an experimental study with real consumers and mobile discount coupons to evaluate our solution. We compared the CBRF algorithm to the widely used context-aware matrix factorization (CAMF) algorithm. The experimental results suggest that incorporating personalized discount sensitivity significantly improves the consumption prediction accuracy and that the suggested CBRF algorithm provides better prediction results for this use case.