Probabilistic group recommendation model for crowdfunding domains

Vineeth Rakesh, Wang Chien Lee, Chandan K. Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

Crowdfunding has gained a widespread popularity by fueling the creative minds of entrepreneurs. Not only has it democratized the funding of startups, it has also bridged the gap between the venture capitalists and the entrepreneurs by providing a plethora of opportunities for people seeking to invest in new business ventures. Nonetheless, despite the huge success of the crowdfunding platforms, not every project reaches its funding goal. One of the main reasons for a project's failure is the difficulty in establishing a linkage between it's founders and those investors who are interested in funding such projects. A potential solution to this problem is to develop recommendation systems that suggest suitable projects to crowdfunding investors by capturing their interests. In this paper, we explore Kickstarter, a popular reward-based crowdfunding platform. Being a highly heterogeneous platform, Kickstarter is fuelled by a dynamic community of people who constantly interact with each other before investing in projects. Therefore, the decision to invest in a project depends not only on the preference of individuals, but also on the inuence of groups that a person belongs and the on-going status of the projects. In this paper, we propose a probabilistic recommendation model, called CrowdRec, that recommends Kickstarter projects to a group of investors by incorporating the on-going status of projects, the personal preference of individual members, and the collective preference of the group. Using a comprehensive dataset of over 40K crowdfunding groups and 5K projects, we show that our model is effective in recommending projects to groups of Kickstarter users.

Original languageEnglish (US)
Title of host publicationWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages257-266
Number of pages10
ISBN (Electronic)9781450337168
DOIs
StatePublished - Feb 8 2016
Event9th ACM International Conference on Web Search and Data Mining, WSDM 2016 - San Francisco, United States
Duration: Feb 22 2016Feb 25 2016

Publication series

NameWSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining

Other

Other9th ACM International Conference on Web Search and Data Mining, WSDM 2016
CountryUnited States
CitySan Francisco
Period2/22/162/25/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Computer Networks and Communications

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