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首页> 外文会议>IEEE International Conference on Data Mining >To be or Not to be Friends: Exploiting Social Ties for Venture Investments
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To be or Not to be Friends: Exploiting Social Ties for Venture Investments

机译：成为或不成为朋友：利用社交关系进行风险投资

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Recent years have witnessed the boom of venture capital industry. Venture capitalists can attain great financial rewards if their invested companies exit successfully, via being acquired or going IPO (Initial Public Offering). The literature has revealed that, from both financial and managerial perspectives, decision-making process and successful rates of venture capital (VC) investments can be greatly improved if the investors well know the team members of target startups. However, much less efforts have been made on understanding the impact of prominent social ties between the members of VC firms and start-up companies on investment decisions. To this end, we propose to study such social relationship and see how this information can contribute to foreseeing investment deals. We aim at providing analytical guidance for the venture capitalists in choosing right investment targets. Specifically, we develop a Social-Adjusted Probabilistic Matrix Factorization (PMF) model to exploit members social connections information from VC firms and startups for investment recommendations. Unlike previous studies, we make use of the directed relationship between any pair of connected members from the two institutions respectively and quantify the variety of social network groups. As a result, it brings in much more flexibility, and the modeling results inherently provide meaningful managerial implications for the operators of VC firms and startups. Finally, we evaluate our model on both synthetic and real-world data. The results demonstrate that our approach outperforms the baseline algorithms with a significant margin.
机译：近年来见证了风险投资行业的蓬勃发展。如果风险投资家通过其被收购公司或通过IPO（首次公开募股）成功退出公司，则可以获得丰厚的财务回报。文献表明，从财务和管理角度来看，如果投资者充分了解目标创业公司的团队成员，则决策过程和风险资本（VC）投资的成功率将得到极大改善。但是，在了解风险投资公司的成员与初创公司之间的重要社会关系对投资决策的影响方面所做的工作少得多。为此，我们建议研究这种社会关系，并了解这些信息如何有助于预见投资交易。我们旨在为风险投资家选择正确的投资目标提供分析指导。具体来说，我们开发了一种社会调整概率矩阵分解（PMF）模型，以利用来自VC公司和初创公司的成员社会联系信息来提出投资建议。与以前的研究不同，我们分别利用两个机构的任何一对关联成员之间的有向关系，并量化了社交网络群体的多样性。结果，它带来了更大的灵活性，并且建模结果固有地为风险投资公司和初创企业的经营者提供了有意义的管理意义。最后，我们在综合和真实数据上评估我们的模型。结果表明，我们的方法明显优于基线算法。

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