A content-based recommender system for e-commerce offers and coupons
A Content-based Recommender System for E-commerceOffers and Coupons
Reward-based e-commerce companies expose thousands of online offers and coupons every day. Customers who signed up for online coupon services either receive a daily digest email with selected offers or select specific offers on the company website front-page. High-quality online discounts are selected and delivered through these two means by applying a manual process that involves a team of experts who are responsible for evaluating recency, product popularity, retailer trends, and other business-related criteria. Such a process is costly, time-consuming, and not customized on users’ preferences or shopping history. In this work, we propose a content- based recommender system that streamlines the coupon selection process and personalizes the recommendation to improve the click- through rate and, ultimately, the conversion rates. When compared to the popularity-based baseline, our content-based recommender system improves F-measures from 0.21 to 0.85 and increases the estimated click-through rate from 1.20% to 7.80%. The experimental system is currently scheduled for A/B testing with real customers.