poster

An empirical study of top-n recommendation for venture finance

Authors:

Thomas Stone,

Weinan Zhang,

Xiaoxue ZhaoAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1865 - 1868

https://doi.org/10.1145/2505515.2507882

Published: 27 October 2013 Publication History

Get Access

Abstract

This paper concerns the task of top-N investment opportunity recommendation in the domain of venture finance. By venture finance, specifically, we are interested in the investment activity of venture capital (VC) firms and their investment partners. We have access to a dataset of recorded venture financings (i.e., investments) by VCs and their investment partners in private US companies. This research was undertaken in partnership with Correlation Ventures, a venture capital firm who are pioneering the use of predictive analytics in order to better inform investment decision making. This paper undertakes a detailed empirical study and data analysis then demonstrates the efficacy of recommender systems in this novel application domain.

References

[1]

L. A. Adamic and B. A. Huberman. Power-law distribution of the world wide web. Science, 2000.

Google Scholar

[2]

S. Anthony. Is Venture Capital Broken?, 2012.

Google Scholar

[3]

T. Bhaskar and G. Subramanian. Loan recommender system for microfinance loans: Increasing efficiency to assist growth. Journal of Financial Services Marketing, 15(4), 2011.

Crossref

Google Scholar

[4]

S. Bhojraj, C. M. C. Lee, and D. K. Oler. Journal of Accounting Research.

Google Scholar

[5]

M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM TOIS, 2004.

Digital Library

Google Scholar

[6]

V. H. Fried and R. D. Hisrich. Toward a Model of Venture Capital Investment Decision Making. Financial Management, 23(3):28--37, 1994.

Crossref

Google Scholar

[7]

Z. Gantner and S. Rendle. MyMediaLite: A free recommender system library. 2011.

Digital Library

Google Scholar

[8]

GroupLens Research. MovieLens Data Sets, 2013.

Google Scholar

[9]

A. Metrick. Venture Capital and the Finance of Innovation. 2007.

Google Scholar

[10]

M. Montaner. A Taxonomy of Recommender Agents on the Internet. pages 285--330, 2003.

Digital Library

Google Scholar

[11]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009.

Digital Library

Google Scholar

[12]

S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-aware recommendations with factorization machines. SIGIR, 2011.

Digital Library

Google Scholar

[13]

T. T. Tyebjee and A. V. Bruno. A Model of Venture Capitalist Investment Activity. Management Science, 1984.

Google Scholar

[14]

Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms. Chapman & Hall, 2012.

Digital Library

Google Scholar

Cited By

View all

Xu RChen HZhao J(2023)SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup RecommendationsJournal of Management Information Systems10.1080/07421222.2023.219677140:2(655-682)Online publication date: 17-Jun-2023
https://doi.org/10.1080/07421222.2023.2196771
Wu SChen RWei GGao XHuo L(2021)Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information NetworkComplexity10.1155/2021/66571912021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6657191
Wang HChen S(2020)A Bipartite Graph-Based Recommender for Crowdfunding with Sparse DataBanking and Finance10.5772/intechopen.92781Online publication date: 23-Dec-2020
https://doi.org/10.5772/intechopen.92781
Show More Cited By

Index Terms

An empirical study of top-n recommendation for venture finance
1. Information systems
  1. Information retrieval

Recommendations

Risk-Hedged Venture Capital Investment Recommendation
RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

With the increasing accessibility of transactional data in venture finance, venture capital firms (VCs) face great challenges in developing quantitative tools to identify new investment opportunities. Recommendation techniques have the possibility of ...
Essays in Empirical Corporate Finance
Essays in Empirical Corporate Finance

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

Acceptance Rates

CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
374
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)3

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Xu RChen HZhao J(2023)SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup RecommendationsJournal of Management Information Systems10.1080/07421222.2023.219677140:2(655-682)Online publication date: 17-Jun-2023
https://doi.org/10.1080/07421222.2023.2196771
Wu SChen RWei GGao XHuo L(2021)Understanding the Impact of Startups’ Features on Investor Recommendation Task via Weighted Heterogeneous Information NetworkComplexity10.1155/2021/66571912021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6657191
Wang HChen S(2020)A Bipartite Graph-Based Recommender for Crowdfunding with Sparse DataBanking and Finance10.5772/intechopen.92781Online publication date: 23-Dec-2020
https://doi.org/10.5772/intechopen.92781
Boytcheva STagarev A(2019)Company Investment Recommendation Based on Data Mining TechniquesBusiness Information Systems Workshops10.1007/978-3-030-36691-9_7(73-84)Online publication date: 17-Dec-2019
https://doi.org/10.1007/978-3-030-36691-9_7
Sharchilev BRoizner MRumyantsev AOzornin DSerdyukov Pde Rijke MCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Web-based Startup Success PredictionProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3272011(2283-2291)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3272011
Zhao XZhang WWang JWerthner HZanker MGolbeck JSemeraro G(2015)Risk-Hedged Venture Capital Investment RecommendationProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2800181(75-82)Online publication date: 16-Sep-2015
https://dl.acm.org/doi/10.1145/2792838.2800181

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Risk-Hedged Venture Capital Investment Recommendation

Essays in Empirical Corporate Finance

Essays in Empirical Corporate Finance