list_of_recommender_systems
recommenders
| list_of_recommender_systems | recommenders | |
|---|---|---|
| 3 | 6 | |
| 4,445 | 18,085 | |
| - | 1.5% | |
| 0.0 | 9.5 | |
| 2 months ago | 14 days ago | |
| Python | ||
| - | 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.
list_of_recommender_systems
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How to approach Recommendation System Project [P]
Hi u/AB3NZ, I think you'd find this repository on a list of recommender systems and resources. A few others:
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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.
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how to make a post recommendation algorithm?
They likely use proprietary ML (machine learning) models for that, but you can check out https://github.com/grahamjenson/list_of_recommender_systems
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?
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
azure-devops-python-api - Azure DevOps Python API
python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
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]
Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis
horapy - 🐍 Python bidding for the Hora Approximate Nearest Neighbor Search Algorithm library
UltraDict - Sychronized, streaming Python dictionary that uses shared memory as a backend
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
Surprise - A Python scikit for building and analyzing recommender systems
ranking - Learning to Rank in TensorFlow
Manim-Tutorial - A tutorial for manim, a mathematical animation engine made by 3b1b