基于用户信息融合的图卷积网络报表推荐算法

doi:10.12204/j.issn.1000-7229.2023.05.005

摘要/Abstract

摘要：

传统报表工具无法自主为企业用户推荐相关业务信息,这一缺陷给电力企业经营管理带来了诸多挑战,针对该问题,提出了基于用户信息融合的图卷积网络报表推荐算法。首先,介绍了以数据中台为底座的报表工具整体架构。在此基础上,以营销业务为例,分析了用户与指标间的关联关系,并提出了基于图卷积网络的关联特征提取模型,同时在模型中融合了用户和指标的一般偏好特征,进一步提升了图聚合信息的深度,进而准确预测指标得分,并给出推荐结果。最后,在公开数据集和营销业务数据集上分别进行模型对比,并选取平均准确率、召回率和归一化折损累计增益作为评价指标,验证所提算法的准确性。结果表明,所提算法与现有算法相比,推荐效果有较大提升,可赋能电力企业经营管理,助力企业数字化转型。

关键词: 报表工具, 图卷积网络, 营销业务, 用户信息

Abstract:

Traditional reporting tools cannot recommend relevant business information to users independently. This has created many challenges in the operation and management of electric power enterprises. To address the above problems, this study proposes a graph convolutional network (GCN) report recommendation algorithm based on user information. First, the overall structure of the reporting tool based on the data center platform is introduced. Considering the marketing business as an example, the correlation between users and indicators is analyzed and a correlation feature extraction model based on a graph convolutional network is proposed. Simultaneously, the general preference characteristics of users and indicators are integrated into the model, and the depth of the graph aggregation information is further improved. Subsequently, the index score is accurately predicted, and a recommendation result is provided. Finally, the models are compared for a public dataset and marketing business dataset, and the average precision rate, recall rate, and normalized discounted cumulative gain are selected as evaluation indicators to verify the accuracy of the proposed algorithm. The results show that compared with existing algorithms, the proposed algorithm significantly improves the recommendation effect, which can empower the operation and management of electric power enterprises and help realize digital transformation.

Key words: report tool, graph convolution network, marketing business, user information

中图分类号:

TM714

杨智伟, 张帆, 杨志, 李文娟, 刘文. 基于用户信息融合的图卷积网络报表推荐算法[J]. 电力建设, 2023, 44(5): 43-52.

YANG Zhiwei, ZHANG Fan, YANG Zhi, LI Wenjuan, LIU Wen. Research on Report Recommendation Algorithm Based on Graph Convolution Network and User Information[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(5): 43-52.

图/表 10

图1 报表工具支撑平台架构

Fig.1 Report tool support platform architecture

图2 电费规范度指标的关联关系

Fig.2 Correlation of electricity tariff standardization indicators

图3 推荐算法的主要思路

Fig.3 Main idea of recommendation algorithm

图4 推荐算法的网络架构

Fig.4 Network architecture of recommendation algorithm

图5 BERT模型基本架构

Fig.5 Basic architecture of BERT model

表1 模型参数

Table 1 Model parameters

表2 公开数据集下性能对比

Table 1 Performance comparison under public datasets

图6 营销数据集下性能对比

Fig.6 Performance comparison under marketing data set

图7 用户32的推荐流程

Fig.7 Recommendation process of user 32

表3 超参数对比实验

Table 3 Comparison experiment of super parameters

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