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基于意图感知知识图的模型用于电力材料推

Lin Zhao¹, Ning Luan¹, Weihua Cheng¹

¹Jiangsu Electric Power Information Technology Co. Ltd, Nanjing, China.

PeerJ. Computer science

|September 24, 2025

概括

本研究介绍了用于推电气材料的意图意识知识图模型. 该模型通过捕捉用户的意图和偏好来提高准确性,优于现有的方法.

科学领域:

电力材料管理电力材料管理
推系统是一个推系统.
人工智能的人工智能

背景情况:

准确的电气材料建议对于有效管理至关重要.
图形神经网络 (GNN) 通过整合节点信息和结构来增强建议.
目前基于GNN的系统缺乏明确的用户意图建模,限制了性能.

研究的目的:

为电气材料推提出一个基于意图的知识图表模型 (IKG-EMR).
解决当前GNN在捕获用户意图方面的局限性.
提高电气材料建议的准确性和有效性.

主要方法:

开发了基于意向意识知识图的电气材料推 (IKG-EMR) 模型.
在三方图 (User-Item-Topic) 上使用图形神经网络 (GNN) 进行意图和项目嵌入.
采用多头注意网络 (变压器) 来从行为序列中提取用户偏好.
集成的用户偏好和意图功能,使用与注意力网络的自适应融合.

主要成果:

拟议的IKG-EMR模型有效地模拟了用户的偏好和意图.
在现实生活中的电力材料上的实验结果表明,与最先进的方法相比,性能优越.
通过结合用户意图,IKG-EMR实现了提升推准确度.

结论:

该IKG-EMR模型为电气材料推系统提供了显著的进步.
显式建模用户意图是提高推质量的关键.
该方法为电力材料管理中的个性化建议提供了一个强大的框架.

关键词:

图形神经网络是一个神经网络.推者系统推者系统主题模型主题模型主题模型变压器变压器变压器

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