Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network
Investor recommendation is a critical and challenging task for startups, which can assist startups in locating suitable investors and enhancing the possibility of obtaining investment. While some efforts have been made for investor recommendation, few of them explore the impact of startups’ features...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6657191 |
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| _version_ | 1850164834555920384 |
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| author | Sen Wu Ruojia Chen Guiying Wei Xiaonan Gao Lifang Huo |
| author_facet | Sen Wu Ruojia Chen Guiying Wei Xiaonan Gao Lifang Huo |
| author_sort | Sen Wu |
| collection | DOAJ |
| description | Investor recommendation is a critical and challenging task for startups, which can assist startups in locating suitable investors and enhancing the possibility of obtaining investment. While some efforts have been made for investor recommendation, few of them explore the impact of startups’ features, including partners, rounds, and fields, to investor recommendation performance. Along this line, in this paper, with the help of the heterogeneous information network, we propose a FEatures’ COntribution Measurement approach of startups on investor recommendation, named FECOM. Specifically, we construct the venture capital heterogeneous information network at first. Then, we define six venture capital metapaths to represent the features of startups that we focus on. In this way, we can measure the contribution of startups’ features on the investor recommendation task by validating the recommendation performance based on different metapaths. Finally, we extract four practical rules to assist in further investment tasks by using our proposed FECOM approach. |
| format | Article |
| id | doaj-art-e90577e2de684dd0be93db81cd429c05 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e90577e2de684dd0be93db81cd429c052025-08-20T02:21:53ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66571916657191Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information NetworkSen Wu0Ruojia Chen1Guiying Wei2Xiaonan Gao3Lifang Huo4School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Economics and Management, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Economics and Management, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Economics and Management, University of Science and Technology Beijing, Beijing 100083, ChinaSchool of Economics and Management, University of Science and Technology Beijing, Beijing 100083, ChinaInvestor recommendation is a critical and challenging task for startups, which can assist startups in locating suitable investors and enhancing the possibility of obtaining investment. While some efforts have been made for investor recommendation, few of them explore the impact of startups’ features, including partners, rounds, and fields, to investor recommendation performance. Along this line, in this paper, with the help of the heterogeneous information network, we propose a FEatures’ COntribution Measurement approach of startups on investor recommendation, named FECOM. Specifically, we construct the venture capital heterogeneous information network at first. Then, we define six venture capital metapaths to represent the features of startups that we focus on. In this way, we can measure the contribution of startups’ features on the investor recommendation task by validating the recommendation performance based on different metapaths. Finally, we extract four practical rules to assist in further investment tasks by using our proposed FECOM approach.http://dx.doi.org/10.1155/2021/6657191 |
| spellingShingle | Sen Wu Ruojia Chen Guiying Wei Xiaonan Gao Lifang Huo Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network Complexity |
| title | Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network |
| title_full | Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network |
| title_fullStr | Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network |
| title_full_unstemmed | Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network |
| title_short | Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information Network |
| title_sort | understanding the impact of startups features on investor recommendation task via weighted heterogeneous information network |
| url | http://dx.doi.org/10.1155/2021/6657191 |
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