CSP

The Steam Engine: A Recommendation System for Steam Users (CIS 520 Final Project)

About

Steam is a video game distribution platform. We employ neighborhood, matrix factorization, and mixed collaborative filtering (CF) methods to predict the number of hours Steam users will play games. We also adapt a regression boosting framework for matrix factorization CF algorithms and apply it to the prediction task. We find that neighborhood methods outperform matrix factorization methods, and a mixed approach outperforms both. Additionally, we find the boosting framework did not meaningful improve performance. To improve predictions, future research should incorporate user friendship networks.

Team Members

• Brandon Lin
• Chris Painter
• Barry Plunkett
• Stephanie Shi

File Directory

• neighborhood - memory-based methods
• factorization - latent factor models
• boost - boosting
• ensemble - mixed methods

Setting up the Project

Installing the Dependencies

pip install -r requirements.txt

Full original dataset can be found here and is over 200GB. Processed data is too large to include and can be obtained by contacting the owner.