基于Vague值的电子商务推荐系统及其相似度研究

针对目前电子商务推荐系统中存在的核心问题--相似度，提出借助Vague集理论研究推荐系统的思想。电子商务过程中顾客行为不确定性的存在，为Vague集的引入提供理论基础。商品推荐依赖的是商品间或顾客间的相似程度，而相似度的计算正是Vague集研究较为成熟的一个领域。根据一般电子商务购物方式，确定不同的顾客类型，在顾客分类的基础上，利用统计方法定义商品的Vague值，实现电子商务推荐系统与Vague的完美结合，并通过相似度的计算验证该方法的有效性，从而为推荐系统的研究提供新的思路和方法。

Recommender Systems of EC and Its Similarity Based on Vague Value

In the paper,vague set theory is introduced into the study of recommender systems to solve its core problem which is similarity.The existence of uncertainty of customer behavior in the course of e-commerce provides a theoretical basis for the introduction of Vague set.Recommendation of goods relies on the degree of similarity between customers or goods,while the calculation of similarity is a mature area in the research of Vague set.First,Different customer types are identified according to the general shopping way in e-commerce.Then based on the customer classification,statistical methods are used to define the Vague value of the commodity.This method makes a perfect combination of e-commerce recommendation system and Vague set,and provides new idea for the study of e-commerce recommendation system.