基于用户行为数据的电网企业营销推荐系统设计

Design of Marketing Recommendation System for Power Grid Enterprises Based on User Behavior data

由于目前方法未能分析和挖掘电网用户行为,使用户的商品属性偏好与预计营销偏好存在差异,导致电网企业营销推荐结果不理想,为此提出基于用户行为数据的电网企业营销推荐系统。通过系统硬件和软件相互协作设计,从用户历史行为出发,优先分析处理用户的历史交互行为,对用户的行为喜好进行分类,挖掘用户的商品属性偏好,实现用户近期需求预测以及意向商品推荐。实验结果证明,所设计系统能够有效提升推荐速率和用户满意度,获取效果较好的推荐结果。

Because the current method fails to analyze and mine the power grid user behavior,there is a difference between the user's commodity attribute preference and the expected marketing preference,resulting in the unsatisfactory marketing recommendation results of power grid enterprises.Therefore,a power grid enterprise marketing automation recommendation system based on user behavior data is proposed,which is designed through the cooperation of system hardware and software.Starting from the user's historical behavior,it gives priority to the analysis and processing of the user's historical interaction behavior,classifies the user's behavior preferences,mines the user's commodity attribute preferences,and realizes the user's recent demand prediction and intended commodity recommendation.The experimental results show that the designed system can effectively improve the recommendation rate and user satisfaction,and obtain better recommendation results.