基于图卷积网络的新型知识图谱推荐算法

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-03-12 DOI:10.1080/09540091.2024.2327441

Hui Guo, Chengyong Yang, Liqing Zhou, Shiwei Wei

引用次数: 0

摘要

知识图谱（KG）被广泛应用于许多领域，尤其是在推荐系统中发挥着重要作用。知识图谱通常需要是完整的，缺失了用户与用户之间的关系。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network

Knowledge Graphs (KGs) are widely used in many fields of application, and especially play an essential role in recommendation systems. KGs often need to be complete, missing relationships between u...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.