A Bayesian bandit approach to personalized online coupon recommendations

[摘要] A digital coupon distributing firm selects coupons from its coupon pool and posts them online for its customers to activate them. Its objective is to maximize the total number of clicks that activate the coupons by sequential arriving customers. This paper resolves this problem by using a multi-armed bandit approach to balance the exploration (learning customers;; preference for coupons) with exploitation (maximizing short term activation clicks). The proposed approach is evaluated with synthetic data. Results showed a 60% click lift compared to the benchmark approach.

[发布机构] Massachusetts Institute of Technology