Demand response intelligence recommendation based on knowledge graph and knowledge graph convolutional neural network

Shizhen Hu, Guangdi Li, Haoyi Wang, Hongyuan Ma, Ziwen Li
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引用次数: 0

Abstract

A demand response intelligent recommendation model integrating knowledge graph and knowledge graph neural network(KGCN) is proposed to address the problems of cold start and sparsity of existing demand response intelligent recommendation algorithms. The structured triad of users’ electricity consumption is extracted from the users’ electricity consumption data set, followed by clustering users with similar electricity consumption behaviors through an improved clustering algorithm, and adding the clustering results to the knowledge graph together with the structured triad, using the KGCN model to embed the neighborhood entity information into the vector space to solve the data sparsity problem; meanwhile, using the prior knowledge in the graph to solve the cold start problem; to solve the To solve the recommendation lag problem, multi-hop propagation algorithm is introduced to reduce the set of candidate users and improve the recommendation efficiency. The results show that the intelligent recommendation model based on KGCN and knowledge graph can effectively solve the above problems and improve the indexes compared with the existing traditional algorithms.
基于知识图和知识图卷积神经网络的需求响应智能推荐
针对现有需求响应智能推荐算法存在冷启动和稀疏性等问题,提出了一种集成知识图和知识图神经网络(KGCN)的需求响应智能推荐模型。从用户用电量数据集中提取用户用电量的结构化三元组,通过改进的聚类算法对具有相似用电量行为的用户进行聚类,并将聚类结果与结构化三元组一起加入到知识图中,利用KGCN模型将邻域实体信息嵌入到向量空间中,解决数据稀疏性问题;同时,利用图中的先验知识解决冷启动问题;为了解决推荐滞后问题,引入多跳传播算法,减少候选用户集,提高推荐效率。结果表明,与现有的传统推荐算法相比,基于KGCN和知识图的智能推荐模型可以有效地解决上述问题,并提高了推荐指标。
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