Uncertainty Management in Personalized Recommendation for E-Commerce

In an e-commerce environment, the magnitude of products and the diversity of venders cause confusion and difficulty to choose the right product from a trustworthy vender. Although people have recognized the importance of feedbacks and reputations for the trustworthiness of individual venders and products, they remain uncertain when they have to define and enforce their own rules to use these different types of information. This paper introduces fuzzy logic into policy definition and enforcement in personalized recommendation for shopping decisions. Applications of this system into different e-commerce environments such as e-shopping and e-auction sites show that this novel tool can provide a convenient and accurate recommendation platform for common shoppers in the e-commerce environment.