蔬菜销售电子商务平台的推荐系统研究

Research on Recommendation System Based on Vegetable Sales E-commerce Platform

应用中的区域蔬菜电子商务系统大多具备网上购销的主要功能,但在提高销售量和消费者应用体验方面有重要作用的推荐系统则建设不多,或不够完善。从用户管理和交易行为特点数据中提取用户特征和购销行为模型,生成k—均值聚类簇和簇中心,计算新用户相应信息与聚类簇中的最近邻居集数据的最大相似度,生成销售推荐信息,可构建出适合区域蔬菜电子商务网站用户规模和应用的推荐系统。

The local vegetable E-commerce systems mostly have the main function of Internet marketing, but the construction of recommendation systems which plays the important roles in the aspects of improvement of sales and consumer application experiences are not many or inadequate. This paper extracts the user characteristics and buying/selling behavior model from the user management and trading behavior characteristic data to build the k-means cluster and cluster center, and calculates the maximum similarity of the nearest neighbor set data in the relevant information of new users and clusters to generate the sales recommendation information, in order to build the recommendation systems which are suitable for local vegetable E-commerce website user scale and application.