An Initial Investigation of Multi-Cyclic Training Regimen for Collaborative Filtering Models in GraphChi

摘要

More and more outlets are utilizing collaborative filtering techniques to make sense of the sea of data generated by our hyper-connected world. How a collaborative filtering model is generated can be the difference between accurate or flawed predictions. This study is to determine the impact of a cyclical training regimen on the algorithms presented in the Collaborative Filtering Toolkit for GraphChi.