A Recommender for Targeted Advertisement of Unsought Products in E-Commerce

Abstract

Recommender systems are a powerful tool for promoting sales in electronic commerce. An effective shopping recommender system can help boost the retailer's sales by reminding customers to purchase additional products originally not on their shopping lists. Existing recommender systems are designed to identify the top selling items, also called hot sellers, based on the store's sales data and customer purchase behaviors. It turns out that timely reminders for unsought products, which are cold sellers that the consumer either does not know about or does not normally think of buying, present great opportunities for significant sales growth. In this paper, we propose the framework and process of a recommender system that identifies potential customers of unsought products using boosting-SVM. The empirical results show that the proposed approach provides a promising solution to targeted advertisement for unsought products in an e-commerce environment.