| 翟千惠,李 明,蔡 潇,程雅梦,俞 阳,朱 萌.基于改进协同过滤算法的电力营销渠道引流策略[J].电力需求侧管理,2023,25(4):105-109 |
| 基于改进协同过滤算法的电力营销渠道引流策略 |
| Power marketing channel diversion strategy based on improved collaborative filtering algorithm |
| 投稿时间：2023-02-10 修订日期：2023-04-17 |
| DOI：10. 3969 / j. issn. 1009-1831. 2023. 04. 017 |
| 中文关键词: 电力营销 数字化转型 改进协同过滤算法 Kmeans算法 K-最近邻矩阵 |
| 英文关键词: power market digital transformation improved collaborative filtering algorithm K- means algorithm K- nearest neighbor matrix |
| 基金项目:国网江苏省电力有限公司科技项目（J2020117） |
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| 中文摘要: |
| 电力企业在数字化转型过程中，打造多渠道服务体系，充分利用“互联网+实体渠道”的方式，可以有效降低企业运营成本，为客户提供更加便捷、高效的服务。在上述背景下，提出了一种基于改进协同过滤算法的电力营销渠道引流策略，首先构造客户-属性数据矩阵，采用矩阵分解算法对原始客户属性矩阵中的缺失数据进行恢复，利用K-means算法对客户属性进行聚类。然后，利用客户混合类型属性相异性度量，通过基于用户的协同过滤推荐算法，寻找目标客户的K-最近邻矩阵，并制定出差异化的引流策略。最后以10万条缴费工单数据为例，分析了客户属性矩阵填充、不同度量方法与最近邻数目对引流准确率的影响，验证了所提算法的有效性和可行性。 |
| 英文摘要: |
| In the process of digital transformation, electric power enterprises can effectively reduce their operating costs and provide customers with more convenient and efficient services by building a multi- channel service system and making full use of the Internet + physical channels. Under the above background, a power marketing channel diversion strategy based on improved collaborative filtering algorithm is proposed. Firstly, the customer attribute data matrix is constructed, and the matrix decomposition algorithm is used to recover the missing data in the original customer attribute matrix, and the K-means algorithm is used to cluster the customer attributes. Then, using the customer mixed type attribute dissimilarity measure, through the user based collaborative filtering recommendation algorithm, the target customer’s K- nearest neighbor matrix is found, and the diversion strategy of travel alienation is formulated.Finally, taking 100 000 payment work order data as an example, the influence of customer attribute matrix filling, different measurement methods and the number of nearest neighbors on the drainage accuracy are analyzed, and the effectiveness and feasibility of the proposed algorithm are found. |
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