基于网络心理挖掘的个性化推荐系统

A novel personalized recommendation system based on network psychology mining

传统的电子商务推荐系统挖掘时效性较差,客户信息浪费较多;为应对这些问题,提出了基于网络心理挖掘的商品推荐系统.该系统通过网站内的商品属性心理特征挖掘和客户心理行为进行挖掘,并结合客户浏览行为对客户的需求和决策进行量化与预测;最终以上述信息为依据,从系统的数据库中提取推荐商品与客户信息.实验表明,该系统能够较好地挖掘客户消费心理,提高商品推荐的精准性和有效性.

In order to deal with the large delay and wasting of the traditional e-commerce systems,a novel recommendation system is presented based on network psychology.Based on the extracting sets between product psychology characters and the customer psychology behavior,the system searches customer decision and requests from their browse processes.And logical timestamps are utilized to guarantee the time sequence.Furthermore,recommended product lists are generated from e-commerce databases.Analysis and simulation results show that it has performed well in customer psychology mining,and increased recommendation precision and effect.