A Collaborative Approach to Angel and Venture Capital Investment Recommendations

07/26/2018
by   Xinyi Liu, et al.
0

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 model, the same dataset was used to generate investor recommendations for companies undergoing fundraising, which achieved highest prediction accuracy of 11.1

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/01/2023

Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting

In this work we approach attractor neural networks from a machine learni...
research
03/05/2015

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

Despite having various attractive qualities such as high prediction accu...
research
08/25/2019

Scalable Probabilistic Matrix Factorization with Graph-Based Priors

In matrix factorization, available graph side-information may not be wel...
research
09/08/2020

Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People

In the context of time-series forecasting, we propose a LSTM-based recur...
research
04/08/2020

Federated Multi-view Matrix Factorization for Personalized Recommendations

We introduce the federated multi-view matrix factorization method that e...
research
01/03/2017

New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data

In this paper, prediction for linear systems with missing information is...
research
01/28/2021

The fraud loss for selecting the model complexity in fraud detection

In fraud detection applications, the investigator is typically limited t...

Please sign up or login with your details

Forgot password? Click here to reset