Which Companies are Likely to Invest: Knowledge-graph-based Recommendation for Investment Promotion

被引:2
作者
Bu, Chenyang [1 ]
Zhang, Jiawei
Yu, Xingchen
Wu, Le
Wu, Xindong
机构
[1] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Key Lab Knowledge Engn Big Data Minist Educ China, Hefei, Peoples R China
来源
2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) | 2022年
基金
中国国家自然科学基金;
关键词
Investment Promotion; dynamic knowledge graph; local link prediction; embedding;
D O I
10.1109/ICDM54844.2022.00011
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Investment promotion refers to the process by which the government uses disposable resources to attract investors to the region for production and business activities. The existing basic mode of attracting investment is to collect information about enterprises and entrepreneurs through manual methods, determine the target enterprise from the list of enterprises, and then attract investment through visits, negotiations, and other methods. As contacting and visiting companies one by one requires huge amounts of manpower and time, the choice of target companies is critical for attracting investments. However, to the best of our knowledge, no study has conducted research from the perspective of knowledge-graph-based recommendation. In this study, we define the problem of target company recommendation in the process of investment promotion, and analyze the characteristics of the problem and the challenges it faces based on the background of the actual problem. Then, a two-tier model for solving this problem is provided from the perspective of knowledge graph reasoning. Aiming at the problem that the knowledge graph will frequently change, the model is designed based on the idea of combining the advantages of global and local link prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed model.
引用
收藏
页码:11 / 20
页数:10
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