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当前的电子商务推荐方法普遍存在时效性较差、客户信息资源挖掘深度不足等问题。针对上述问题,提出了应用于电子商务推荐系统的新型人工心理方法与模型,给出了相关的模型结构与处理流程。该模型对商品属性和客户操作行为中的心理特征进行量化与分析,并结合客户在购物活动中的相关事件,对客户的未来需求进行挖掘预测,并最终实施推荐。实验表明,基于人工心理模型的推荐系统能够较好的满足客户的心理,从而提高推荐的精准性和有效性。
The current e-commerce recommendation methods are generally poorly time-consuming and the customer information resources are not well-developed. In view of the above problems, a new artificial psychology method and model for e-commerce recommendation system is proposed, and the related model structure and processing flow are given. The model quantifies and analyzes the psychological characteristics of the product attributes and customer operation behaviors. The model predicts the future demand of customers in combination with the related events of customers in shopping activities, and finally implements the recommendation. Experiments show that the recommendation system based on artificial mental model can better meet the customer’s psychology, so as to improve the accuracy and effectiveness of the recommendation.