推荐算法研究进展及知识图谱可视化分析

Research Progress on Recommendation Algorithms with Knowledge Graph Visualization Analysis

互联网技术的应用普及使网络数据资源呈指数级增长,从海量数据中获取需求信息愈加困难。推荐算法因能有效解决信息过载问题而备受关注,相关研究成果层出不穷。以中国知网(CNKI)和科学网(WOS)核心合集为主要数据源,采集2012—2024年间出版的4773篇和4531篇中英文文献,利用可视化分析工具CiteSpace和VOSviewer绘制文献基本信息与关键词共现图谱;借助图谱分析,提炼核心技术关键词:知识图谱、图神经网络和深度学习,并选取与之关联的代表性推荐算法;通过图表直观展示算法核心机制和基本原理,聚焦现有研究的不足与挑战以及针对性解决方案;基于挑战-方案-来源文献的格式,绘制各核心技术关键词所关联算法的知识架构图,实现推荐原理的可视化。

The application and proliferation of internet technology has caused an exponential growth in data,enhancing the complexity of information retrieval from massive datasets.Recommendation algorithms have attracted significant attention for alleviating information overload,with relevant research findings continually emerging.4773 Chinese and 4531 English publications from 2012 to 2024 have been sourced from China National Knowledge Infrastructure(CNKI)and the Web of Science(WOS)core collection.Visualization tools CiteSpace and VOSviewer have been utilized to generate basic information and keyword co-occurrence graphs for literatures.Core technology keywords,including knowledge graph,graph neural network,and deep learning,have been extracted through graph analysis,and the corresponding representative recommendation algorithms have been selected.The core mechanisms and the underlying principles of the algorithms have been visually presented through charts,focusing on the limitations and challenges of existing research,as well as targeted solutions.Knowledge architecture diagrams have been developed for the algorithms associated with each core technology keyword,following the challenge-solution-source literature framework.The visualization of recommendation principles has been effectively implemented.