ranking
recommenders
| ranking | recommenders | |
|---|---|---|
| 1 | 6 | |
| 2,714 | 17,980 | |
| 0.1% | 1.0% | |
| 6.3 | 9.5 | |
| about 2 months ago | 11 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ranking
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[D] learning to Rank
There are many different models and loss functions used for ranking (Tensorflow Ranking offers a bunch, probably also available for Jax / Pytorch / etc., or easily convertible).
recommenders
- My kernel dies when I fit my LightFm model from Microsoft Recommenders
- There is framework for everything.
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This Week in Python
recommenders – Best Practices on Recommendation Systems
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Input to SVD, SAR, NMF
I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.
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Opinion on choice of model - Recommender System
Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.
What are some alternatives?
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
metarank - A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
torchsort - Fast, differentiable sorting and ranking in PyTorch
azure-devops-python-api - Azure DevOps Python API
RecBole - A unified, comprehensive and efficient recommendation library
python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.
BERT-QE - Code and resources for the paper "BERT-QE: Contextualized Query Expansion for Document Re-ranking".
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
spotlight - Deep recommender models using PyTorch.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
rexmex - A general purpose recommender metrics library for fair evaluation.
Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis