SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations

References

• Agrawal, A.; Kapur, D.; and McHale, J. How do spatial and social proximity influence knowledge flows? Evidence from patent data. Journal of Urban Economics, 64, 2 (2008), 258–269.
   Web of Science ®Google Scholar
• Alghowinem, S.; Goecke, R.; Wagner, M.; Epps, J.; Hyett, M.; Parker, G.; and Breakspear, M. Multimodal depression detection: Fusion analysis of paralinguistic, head pose and eye gaze behaviors. IEEE Transactions on Affective Computing, 9, 4 (2016), 478–490.
   Web of Science ®Google Scholar
• Baltrušaitis, T.; Ahuja, C.; and Morency, L.P. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2 (2018), 423–443.
   PubMed Web of Science ®Google Scholar
• Bengtsson, O.; and Hsu, D.H. Ethnic matching in the U.S. venture capital market. Journal of Business Venturing, 30, 2 (2015), 338–354.
   Web of Science ®Google Scholar
• Bethard, S., and Jurafsky, D. Who should I cite: learning literature search models from citation behaviour. In The 19th ACM International Conference on Information and Knowledge Management. Toronto, Ontario, Canada: Association for Computing Machinery (ACM), 2010, pp. 609–618.
   Google Scholar
• Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; and Yakhnenko, O. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems, 2013, pp. 2787–2795.
   Google Scholar
• Cai, H.; Zheng, V.W.; and Chang, K.C.C. A comprehensive survey of graph embedding: problems, techniques, and applications. In IEEE Transactions on Knowledge and Data Engineering, 30, 9 (2018), pp. 1616–1637.
   Web of Science ®Google Scholar
• Clarke, J.; Chen, H.; Du, D.; and Hu, Y.J. Fake news, investor attention, and market reaction. Information Systems Research, 32, 1 (2020), 35–52.
   Web of Science ®Google Scholar
• Cohen, L.; Frazzini, A.; and Malloy, C. The small world of investing: Board connections and mutual fund returns. Journal of Political Economy, 116, 5 (2008), 951–979.
   Web of Science ®Google Scholar
• Cumming, D.; and Dai, N. Local bias in venture capital investments. Journal of Empirical Finance, 17, 3 (2010), 362–380.
   Web of Science ®Google Scholar
• Deshpande, M.; and Karypis, G. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems, 22, 1 (2004), 143–177.
   Web of Science ®Google Scholar
• Franke, N.; Gruber, M.; Harhoff, D.; and Henkel, J. What you are is what you like—similarity biases in venture capitalists’ evaluations of start-up teams. Journal of Business Venturing, 21, 6 (2006), 802–826.
   Web of Science ®Google Scholar
• Friedman, J.H. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 5 (2001), 1189–1232.
   Web of Science ®Google Scholar
• Friedman, J.; Hastie, T.; and Tibshirani, R. The Elements of Statistical Learning, Berlin: Springer, 2001.
   Google Scholar
• Gantner, Z.; Drumond, L.; Freudenthaler, C.; Rendle, S., and Schmidt-Thieme, L. Learning attribute-to-feature mappings for cold-start recommendations. In IEEE International Conference on Data Mining. Sydney, NSW, Australia: IEEE, 2010, pp. 176–185.
   Google Scholar
• Ghoshal, A.; Menon, S.; and Sarkar, S. Recommendations using information from multiple association rules: A probabilistic approach. Information Systems Research, 26, 3 (2015), 532–551.
   Web of Science ®Google Scholar
• Ghoshal, A.; Mookerjee, V.S.; and Sarkar, S. Recommendations and cross-selling: pricing strategies when personalizing firms cross-sell. Journal of Management Information Systems, 38, 2 (2021), 430–456.
   Web of Science ®Google Scholar
• Gomber, P.; Kauffman, R.J.; Parker, C.; and Weber, B.W. On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems 35, 1 (2018), 220–265.
   Web of Science ®Google Scholar
• Gompers, P.A.; Gornall, W.; Kaplan, S.N.; and Strebulaev, I.A. How do venture capitalists make decisions? Journal of Financial Economics, 135, 1 (2020), 169–190.
   Web of Science ®Google Scholar
• Gompers, P.A.; and Lerner, J. The Venture Capital Cycle. Cambridge, MA: MIT Press, 1999.
   Google Scholar
• Gompers, P.A.; Mukharlyamov, V.; and Xuan, Y. The cost of friendship. Journal of Financial Economics, 119, 3 (2016), 626–644.
   Web of Science ®Google Scholar
• Gozman, D.; Liebenau, J.; and Mangan, J. The innovation mechanisms of fintech start-ups: insights from SWIFT’s innotribe competition. Journal of Management Information Systems, 35, 1 (2018), 145–179.
   Web of Science ®Google Scholar
• Gregor, S.; and Hevner, A.R. Positioning and presenting design science research for maximum impact. MIS Quarterly, 37, 2 (2013), 337–355.
   Web of Science ®Google Scholar
• Guo, J.; Zhang, W.; Fan, W.; and Li, W. Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. Journal of Management Information Systems, 35, 4 (2018), 1121–1153.
   Web of Science ®Google Scholar
• Hallen, B.L. The causes and consequences of the initial network positions of new organizations: From whom do entrepreneurs receive investments? Administrative Science Quarterly, 53, 4 (2008), 685–718.
   Web of Science ®Google Scholar
• He, J.; Fang, X.; Liu, H.; and Li, X. Mobile app recommendation: an involvement-enhanced approach. MIS Quarterly, 43, 3 (2019), 827–849.
   Web of Science ®Google Scholar
• Hegde, D.; and Tumlinson, J. Does social proximity enhance business partnerships? Theory and evidence from ethnicity’s role in U.S. venture capital. Management Science, 60, 9 (2014), 2355–2380.
   Web of Science ®Google Scholar
• Hendershott, T.; Zhang, X.; Zhao, J.L.; and Zheng, Z. FinTech as a game changer: Overview of research frontiers. Information Systems Research, 32, 1 (2021), 1–17.
   Web of Science ®Google Scholar
• Hevner, A.R.; March, S.T.; Park, J.; and Ram, S. Design science in information systems research. MIS Quarterly, 28, 1 (2004), 75–105.
   Web of Science ®Google Scholar
• Hochberg, Y.; Ljungqvist,A.; and Lu, Y. Whom you know matters: Venture capital networks and investment performance. Journal of Finance, 62,1 (2007), 266–301.
   Web of Science ®Google Scholar
• Hsu, D. What do entrepreneurs pay for venture capital affiliation? Journal of Finance, 59, 4 (2004), 1805–1844.
   Web of Science ®Google Scholar
• Hu, Y.; Koren, Y., and Volinsky, C. Collaborative filtering for implicit feedback datasets. In The Eighth IEEE International Conference on Data Mining. Sydney, NSW, Australia: IEEE, 2008, pp. 263–272.
   Google Scholar
• Ingram, P.; and Roberts, P.W. Friendships among competitors in the Sydney hotel industry. American Journal of Sociology, 106, 2 (2000), 387–423.
   Web of Science ®Google Scholar
• Ji, G.; He, S.; Xu, L.; Liu, K., and Zhao, J. Knowledge graph embedding via dynamic mapping matrix. In The 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China: Association for Computational Linguistics, 2015, pp. 687–696.
   Google Scholar
• Lerner, J. Venture capitalists and the oversight of private firms. Journal of Finance, 50, 1 (1995), 301–318.
   Web of Science ®Google Scholar
• Lerner J. Corporate venturing. Harvard Business Review, 91, 10, (2013), 86–94.
   Web of Science ®Google Scholar
• Lin, Y.; Liu, Z.; Sun, M.; Liu, Y., and Zhu, X. Learning entity and relation embeddings for knowledge graph completion. In The Twenty-ninth AAAI Conference on Artificial Intelligence. Austin, Texas: Association for the Advancement of Artificial Intelligence, 29, 1 (2015), pp. 2181–2187.
   Google Scholar
• Long, J.; Chen, Z.; He, W.; Wu, T.; and Ren, J. An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market. Applied Soft Computing, 91 (2020), 106–205.
   Web of Science ®Google Scholar
• McPherson, M.; Smith-Lovin, L.; and Cook, J. M. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 1 (2001), 415–444.
   Web of Science ®Google Scholar
• Meng, C.; Cheng, R.; Maniu, S.; Senellart, P., and Zhang, W. Discovering meta-paths in large heterogeneous information networks. In The 24th International Conference on World Wide Web. Florence, Italy: Association for Computing Machinery (ACM), 2015, pp. 754–764.
   Google Scholar
• Müller, O.; Fay, M.; and vom Brocke, J. The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35, 2 (2018), 488–509.
   Web of Science ®Google Scholar
• Nunamaker Jr, J.F.; Briggs, R.O.; Derrick, D.C.; and Schwabe, G. The last research mile: Achieving both rigor and relevance in information systems research. Journal of Management Information Systems, 32, 3 (2015), 10–47.
   Web of Science ®Google Scholar
• Pan, Y.; and Wu, D. A novel recommendation model for online-to-offline service based on the customer network and service location. Journal of Management Information Systems, 37, 2 (2020), 563–593.
   Web of Science ®Google Scholar
• Pathak, B.; Garfinkel, R.; Gopal, R.D.; Venkatesan, R.; and Yin, F. Empirical analysis of the impact of recommender systems on sales. Journal of Management Information Systems, 27, 2 (2010), 159–188.
   Web of Science ®Google Scholar
• Peffers, K.; Tuunanen, T.; Rothenberger, M.A.; and Chatterjee, S. A design science research methodology for information systems research. Journal of Management Information Systems, 24, 3 (2007), 45–77.
   Web of Science ®Google Scholar
• Prat, N.; Comyn-Wattiau, I.; and Akoka, J. A taxonomy of evaluation methods for information systems artifacts. Journal of Management Information Systems, 32, 3 (2015), 229–267.
   Web of Science ®Google Scholar
• Richardson, M.; Dominowska, E., and Ragno, R. Predicting clicks: Estimating the click-through rate for new Ads. In The 16th International Conference on World Wide Web. New York, NY: Association for Computing Machinery (ACM), 2007, pp. 521–530.
   Google Scholar
• Rider, C.I. How Employees’ prior affiliations constrain organizational network change: A study of US venture capital and private equity. Administrative Science Quarterly, 57, 3 (2012), 453–483.
   Web of Science ®Google Scholar
• Sarwar, B.; Karypis, G.; Konstan, J., and Riedl, J. Item-based collaborative filtering recommendation algorithms. In The 10th International Conference on World Wide Web. Hong Kong: Association for Computing Machinery (ACM), 2001, pp. 285–295.
   Google Scholar
• Sharchilev, B.; Roizner, M.; Rumyantsev, A.; Ozornin, D.; Serdyukov, P., and de Rijke, M. Web-based startup success prediction. In The 27th ACM International Conference on Information and Knowledge Management. Torino, Italy: Association for Computing Machinery (ACM), 2018, pp. 2283–2291.
   Google Scholar
• Shi, Z.; Lee, G.M.; and Whinston, A.B. Toward a better measure of business proximity: Topic modeling for industry intelligence. MIS Quarterly, 40, 4 (2016), 1035–1056.
   Web of Science ®Google Scholar
• Shipilov, A.; Gulati, R.; Kilduff, M.; Li, S.; and Tsai, W. Relational pluralism within and between organizations. Academy of Management Journal, 57, 2 (2014), 449–459.
   Web of Science ®Google Scholar
• Sørensen, M. How Smart is Smart Money? A two-sided matching model of venture capital. The Journal of Finance, 62, 6 (2007), 2725–2762.
   Web of Science ®Google Scholar
• Sorenson, O.; and Stuart, T.E. Syndication networks and the spatial distribution of venture capital investments. American Journal of Sociology, 106, 6 (2001), 1546–1588.
   Web of Science ®Google Scholar
• Stone, T.; Zhang, W., and Zhao, X. An empirical study of top-N recommendation for venture finance. In The 22nd ACM International Conference on Information & Knowledge Management. San Francisco, California: Association for Computing Machinery (ACM), 2013, pp. 1865–1868.
   Google Scholar
• Sun, Y.; Barber, R.; Gupta, M.; Aggarwal, C.C., and Han, J. Co-author relationship prediction in heterogeneous bibliographic networks. In The International Conference on Advances in Social Networks Analysis and Mining. Kaohsiung, Taiwan: IEEE, (2011), pp. 121–128.
   Google Scholar
• Sun, Y.; Han, J.; Yan, X.; Yu, P.S.; and Wu, T. PathSim: meta path-based top-k similarity search in heterogeneous information networks. The VLDB Endowment, 4, 11 (2011), 992–1003.
   Google Scholar
• Sun Yin, H.H.; Langenheldt, K.; Harlev, M.; Mukkamala, R.R.; and Vatrapu, R. Regulating cryptocurrencies: a supervised machine learning approach to de-anonymizing the bitcoin blockchain. Journal of Management Information Systems, 36, 1 (2019) 37–73.
   Web of Science ®Google Scholar
• Wang, J.; Huang, P.; Zhao, H.; Zhang, Z.; Zhao, B., and Lee, D.L. Billion-scale commodity embedding for e-commerce recommendation in Alibaba. In The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. London: Association for Computing Machinery (ACM), 2018, pp. 839–848.
   Google Scholar
• Wang, Z.; Zhang, J.; Feng, J., and Chen, Z. Knowledge graph embedding by translating on hyperplanes. In The Twenty-Eighth AAAI Conference on Artificial Intelligence. Québec, Canada: Association for the Advancement of Artificial Intelligence, 2014, pp. 1112–1119.
   Google Scholar
• Webber R. 2007. Using names to segment customers by cultural, ethnic or religious origin. Journal of Direct Data Digital Marketing Practice, 8, 3 ( 2014), 226–242.
   Google Scholar
• WSJ. 2021. VC Firms Have Long Backed AI. Now, They Are Using It. https://www.wsj.com/articles/vc-firms-have-long-backed-ai-now-they-are-using-it-11616670000 (accessed on April 7, 2023).
   Google Scholar
• Xiang, G.; Zheng, Z.; Wen, M.; Hong, J.I.; Rosé, C.P., and Liu, C. A supervised approach to predict company acquisition with factual and topic features using profiles and news articles on TechCrunch. In The Sixth International AAAI Conference on Weblogs and Social Media. Dublin, Ireland: Association for the Advancement of Artificial Intelligence, 2012, pp. 607–610.
   Google Scholar
• Zeng, X.; Li, Y.; Leung, S.C.; Lin, Z.; and Liu, X. Investment behavior prediction in heterogeneous information network. Neurocomputing, 217 (2016), 125–132.
   Web of Science ®Google Scholar
• Zhang, W.; and Ram, S. A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources. MIS Quarterly, 44, 1 (2020), 305–350.
   Web of Science ®Google Scholar
• Zhang, W.; Wei, C.P.; Jiang, Q.; Peng, C.H.; and Zhao, J.L. Beyond the block: A novel blockchain-based technical model for long-term care insurance. Journal of Management Information Systems, 38, 2 (2021), 374–400.
   Web of Science ®Google Scholar
• Zhong, H.; Liu, C.; Zhong, J.; and Xiong, H. Which startup to invest in: A personalized portfolio strategy. Annals of Operations Research, 263, 1–2 (2018), 339–360.
   Web of Science ®Google Scholar