基于套餐隐式评分与用户画像的电力套餐推荐方法

Electricity Plan Recommendation Method Based on Implicit Score of Electricity Plan and User Portrait

面向售电公司提供差异化售电服务、提高用户黏性的市场需求,提出基于套餐隐式评分与用户画像的电力套餐推荐方法。首先,提取电力套餐属性标签,以用户的套餐历史购买行为作为其对电力套餐的隐式评分,构建计及偏好衰减的用户-套餐标签画像模型。然后,以皮尔逊相关系数和欧氏距离分别表征用户的分时负荷与总负荷水平的相似性,提出基于双尺度负荷聚类和轮廓系数的套餐标签赋权方法。在此基础上,构建基于加权欧氏距离的用户画像相似度评估模型,提出电力套餐协同过滤推荐方法,为目标用户筛选并推荐最经济的电力套餐。对不同负荷水平和用电习惯的用户进行电力套餐推荐仿真,结果表明,所提套餐推荐方法可根据用户的套餐历史购买信息发掘其消费偏好,提高售电公司的电力套餐推荐准确率。

For the market demands of electricity retailers in providing differentiated electricity selling services and improving user stickiness, an electricity plan recommendation method based on the implicit score of electricity plan and user portrait is proposed.First, by extracting specified labels to represent the characteristics of electricity plans, the historical purchase behaviors of users are introduced as the implicit score of the corresponding plans, and a label-based user portrait model considering the attenuation of user preference is constructed. Then, the Pearson correlation coefficient and Euclidean distance are used to evaluate the similarity of time-sharing load and total load demand between users, respectively, and a label weighting method based on the two-scale similarity clustering of users′ load profiles and the silhouette coefficient is proposed. On this basis, a user portrait similarity evaluation model is constructed based on the weighted Euclidean distance, and a collaborative filtering based electricity plan recommendation method is proposed to select and recommend the most economical electricity plans for target users. The electricity plan recommendation simulation is carried out for users with different load demands and electricity consumption habits. The results show that the proposed electricity plan recommendation method can explore consumption preferences of users according to their historical purchase information, so as to improve the accuracy of electricity plan recommendation of electricity retailers.