A Collaborative Approach to Angel and Venture Capital Investment Recommendations
This work addresses investment matching in venture capital, but it is incremental as it applies standard methods to a specific dataset without major innovations.
The paper tackled the problem of generating investment recommendations for angel and venture capital investors using matrix factorization, achieving a highest average prediction accuracy of 13.3% for investor recommendations and 11.1% for company recommendations.
Matrix factorization was used to generate investment recommendations for investors. An iterative conjugate gradient method was used to optimize the regularized squared-error loss function. The number of latent factors, number of iterations, and regularization values were explored. Overfitting can be addressed by either early stopping or regularization parameter tuning. The model achieved the highest average prediction accuracy of 13.3%. With a similar model, the same dataset was used to generate investor recommendations for companies undergoing fundraising, which achieved highest prediction accuracy of 11.1%.