Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34,469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies' domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors' domain preference.

中文翻译：

---

通过整合网络扩散和投资者领域偏好为新创企业推荐投资者

在过去的十年中，许多初创公司如雨后春笋般涌现，这对风险投资者的资金支持产生了巨大的需求。然而，由于投资者和公司之间的信息不对称，融资过程通常具有挑战性和耗时，特别是对于尚未获得任何投资的初创公司。因此，非常需要有效的数据驱动技术来自动将初创公司与潜在的相关投资者进行匹配。在这里，我们分析了从 www.itjuzi.com 收集的 34,469 条有效投资事件，并考虑了为新创业公司推荐投资者的冷启动问题。我们通过构建数据的不同三方网络表示来解决这个问题，其中节点代表投资者、公司和公司的域。第一的，我们发现投资者在投资时有很强的领域偏好，这促使我们在三方网络建设中引入投资者和投资领域之间的虚拟链接。我们对应用于各种网络表示的基于扩散的算法的推荐性能的分析表明，通过将网络扩散过程与投资者的领域偏好相结合，可以有效地揭示新创业公司的潜在投资者。