/github-recommendations

GitHub repositories and users recommendations by embeddings

Primary LanguageJupyter NotebookMIT LicenseMIT

GitHub repositories and users recommendations by embeddings

Problem Statement

Currently, GitHub has two possibilities to explore users and repositories:

  1. Direct search by search term leveraging names and tags.
  2. Recommender system under 'Explore' tab which gives suggestions to a user based on his usage of service. However, there is no possibility to perform a search of connected entities. E.g., find repositories or users highly related to each other.

Goal of the Project

The goal of this project is to build GitHub repository search/recommender system, which would allow exploring connected repositories and people, by leveraging the underlying graph structure of the repositories database.

Implemented ML solution

It was decided to build graph nodes embeddings (repo2vec and user2vec) for the entire GitHub database using PyTorch-BigGraph (PBG). On top of the embeddings representation, we have built query tool with the ranking engine.

Data: http://ghtorrent.org/downloads.html

To run our pipeline

  1. Change resources/config.template.json to resources/config.json with your info;
  2. Download SQL dump you like (here we use 2019-06-01) at data/ folder (run db_download.sh script (at terminal));
  3. Run project_notebook.ipynb notebook;
  4. View tb/README.md for more info about TensorBoard launch with prepared embeddings and metadata (docker based, but it is possible to run without it if needed);
  5. Modify code the way you like to find some new insights and share with us!

Demo

Visualizations with different kind of tensors (embeddings) are available at TensorBoard: http://hel.sergibro.me:8002/#projector [hope not to forget to update if it moves] Hints:

  • open from desktop browser (it fetch hundreds of MB for larger tensors and computations done on the client side!);
  • for better visual experience run T-SNE instead of PCA for 500-1K iterations on large tensors with 5-15 perplexity and learning rate set to 1 (from our experience); for smaller tensors you can play more due to fewer computations (but losing in data points);
  • you may choose feature to be colored by (language for repos, type for users, etc.)

How-to perform a search:

Contacts: https://t.me/sergibro