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Final Report
data_curation
.gitignore
LICENSE
README.md
autoencoder.py
create_mappings.py
make_implicit_rating_matrix.py
rating_matrix
repo_mappings.pkl
report.pdf
user_mappings.pkl

README.md

Github-Recommender-System

About

In this work, we used implicit ratings and an auto-encoder with a modified cost function to make a GitHub Recommender System. First, we collect the data, construct the confidence and prediction matrices based on implicit rating schemes. Finally, we train an auto-encoder with a modified cost function and test the trained model using Recall metric.

Files

  • report.pdf - gives detailed methodology and results of the project.
  • data_curation - contains all the scripts used for dataset creation.
  • Final Report - contains the .tex files of the report.pdf.
  • autoencoder.py - contains the autoencoder code.