A Collaborative Approach to Angel and Venture Capital Investment Recommendations

TL;DR

This paper presents a matrix factorization approach using conjugate gradient optimization to generate investment recommendations for angel and venture capital investors, achieving notable prediction accuracy.

Contribution

It introduces a novel application of matrix factorization with conjugate gradient optimization for investment recommendation systems, including methods to prevent overfitting.

Findings

Prediction accuracy of 13.3% for investor recommendations.

Prediction accuracy of 11.1% for company fundraising recommendations.

Effective overfitting mitigation through early stopping and regularization.

Abstract

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%.