Recommending investors for crowdfunding projects

To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others' projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors' Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts

Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

Understanding the Characteristics of Successful Projects and Post-Campaign Activities in a Crowdfunding Platform

Online crowdfunding platforms provide project creators with new opportunities for seeking funds from people in the world. But reaching a fund-raising goal or making a project successful is always a challenge. Besides, little is known about post-campaign activities of project creators and backers. To fill the gap, in this research, we are interested in understanding (i) the characteristics of successful projects, (ii) how project creators reacted when their projects failed, and (iii) what post-campaign activities creators and backers made. To achieve our research objectives, first, we analyzed successful projects and failed projects on Kickstarter, the most popular crowdfunding platform. Then we clustered successful projects by their evolutionary patterns in terms of pledged money toward understanding what efforts project creators should make in order to make a project successful and get more pledged money. We also analyzed what activities project creators and backers made during a post-campaign period by building topic models from comments associated with the projects

Motivations for Financial Backing of Reward Crowdfunding Campaigns - Based on Data from Germany and Norway : A multiple case study

Master thesis Business Administration - University of Agder 201

Resolving the Crowdfunding Conundrum: The Experience of the United States and Spain

The phenomenon known as crowdfunding has become an attractive alternative for businesses looking for investors without having to go through more well-established routes or without necessarily having to lure and impress professional investors. However, this new form of raising capital creates a series of issues and problems unique to crowdfunding, which has led to a struggle amongst governments to effectively regulate this new entrepreneurial opportunity. The crowdfunding conundrum government regulators are facing causes them to have to reconcile two contradictory missions: facilitating the acquisition of capital by businesses and protecting investors (and the market) from fraud and manipulation. This Article analyzes this conundrum from a United States (“U.S.”) and Spanish perspective. I first describe the crowdfunding conundrum in general terms by explaining how crowdfunding (both consumer and accredited investor) works in practice and explore the major problems and issues that startup companies, investors, the market, and the state face in crowdfunding, which need to be resolved in a regulatory system. I will then describe and evaluate the current American and Spanish and proposed European regulatory solutions to the crowdfunding conundrum. I then conclude by evaluating whether and how well the United States’ and Spain’s regulations, as well as the European Union’s (“EU”) proposed regulations, have attempted to resolve the conundrum by balancing the risks and problems facing crowdfunding transactions