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Source code for Twitter's Recommendation Algorithm
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twitter-team b85210863f [home/recap] Update heavy ranker explanation of weighted engagement probabilities 
Update heavy ranker README with:
- Clarification that engagement probability weights aren't hardcoded in the code.
- Explanation of how the engagement probability weights were obtained.
2023-04-05 21:20:38 -05:00
.github/workflows Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
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projects [home/recap] Update heavy ranker explanation of weighted engagement probabilities 2023-04-05 21:20:38 -05:00
reader Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
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.pre-commit-config.yaml Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
COPYING Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
LICENSE.torchrec Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
model.py Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
pyproject.toml Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00
README.md Twitter's Recommendation Algorithm - Heavy Ranker and TwHIN embeddings 2023-03-31 13:05:14 -05:00

README.md

This project open sources some of the ML models used at Twitter.

Currently these are:

  1. The "For You" Heavy Ranker (projects/home/recap).

  2. TwHIN embeddings (projects/twhin) https://arxiv.org/abs/2202.05387

This project can be run inside a python virtualenv. We have only tried this on Linux machines and because we use torchrec it works best with an Nvidia GPU. To setup run

./images/init_venv.sh (Linux only).

The READMEs of each project contain instructions about how to run each project.