基于异构网络嵌入的债券推荐

Jiazhe Zhang, Cui Zhu, Wenjun Zhu
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引用次数: 0

摘要

债券融资已成为企业外部融资的主要方式。然而,很少有研究涉及金融产品对金融机构的建议。以债券为例,金融机构通常需要多种类型的数据来支持它们向企业推销债券。但数据收集难度大,分析量大。因此,本文以发行历史数据为基础,简化所需模型数据,依托公司推荐研究债券发行关系。债券包含多种异质特征,其中包含丰富的信息。因此,本文采用了基于HIN的推荐方法。本文从三个方面进行了改进。首先,设计有意义的元路径,并在随机漫步策略中加入约束条件,使其符合金融领域的应用场景;其次,设计生成策略,生成目标类型节点的同构序列;第三,基于同行业债券推荐,解决了公司的冷启动问题。本文在真实数据集上进行了实验,实验结果表明了该方法的有效性,可以帮助客户经理发现商机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bond Recommendation Based on Heterogeneous Network Embedding
Bond financing has become the main way of external financing. However, few studies have addressed recommendations for financial products to financial institutions. In the case of bonds, financial institutions often need multiple types of data to back up their marketing of bonds to companies. However, it is difficult to collect data and has a large amount of analysis. Therefore, this article based on issuance of historical data, simplifying the model data needed, rely on the company recommended study on the relationship between the issuance of bonds. Bonds contain a variety of heterogeneous characteristics, which contain a wealth of information. Therefore, this paper adopts the recommendation method based on HIN. This paper improves from three aspects. First, a meaningful meta-path is designed and a constraint condition is added to the random walk strategy to make it conform to the application scenario in the financial field. Secondly, the generation strategy is designed to generate isomorphic sequence of node of target type. Thirdly, based on the same industry bond recommendation, this method solves the cold start problem of the company. This paper conducts experiments on real data sets, and experimental results show the effectiveness of this method, which will assist account managers to find business opportunities.
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