Research Progress on Recommendation Algorithms with Knowledge Graph Visualization Analysis
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.4 773 Chinese and 4 531 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 repre-sentative recommendation algorithms have been selected.The core mechanisms and the underlying principles of the algo-rithms 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.
万方数据
维普
