In the Crest catalog
recommenders-team/recommenders
Best Practices on Recommendation Systems
- Stars
- 21.9K
- Forks
- 3.3K
- Language
- Python
- Last push
- 22d ago
- Last release
- 22mo ago
Linked by the package’s own registry metadata.GitHub (opens in a new tab)
Package · PyPI
recommenders
PyPI · v1.2.1 · MIT License
Recommenders - Python utilities for building recommendation systems
- AI & Machine Learning (primary)
- Data Science & Visualization
- Containers & Orchestration
- Data Engineering
- Graphics & 3D · Tentative
Tentative categories were assigned with moderate confidence (0.5 to 0.75): they are shown, but they do not count toward a category’s momentum. How categories work
Install
pip install recommendersAdoption
- Adoption
- Not published by PyPI
- Not published
- Popularity
- Not published by PyPI
- Not published
- Dependents
- Not published by PyPI
- Not published
- Maintenance
- 0.13
- DerivedRelease cadence and recency, 0 to 1
- Releases per year
- 2.0
- 7 versions over its life
Growth by period
PyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.
- 7 days
- Not published by PyPI
- Not published
- 30 days
- Not published by PyPI
- Not published
- 90 days
- Not published by PyPI
- Not published
- 1 year
- Not published by PyPI
- Not published
Adoption history
Adoption history
PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.
Facts from the registry
- Latest version
- v1.2.1
- 7 versions
- First published
- JUL 16, 2021
- Last release
- DEC 24, 2024
- 22mo ago
- Licence
- MIT License
Repository
Dependencies latest version · v1.2.1
Runtime 17
- category-encoders
<3,>=2.6.0 - cornac
<3,>=1.15.2 - hyperopt
<1,>=0.2.7 - lightgbm
<5,>=4.0.0 - locust
<3,>=2.12.2 - memory-profiler
<1,>=0.61.0 - nltk
<4,>=3.8.1 - notebook
<8,>=6.5.5 - numba
<1,>=0.57.0 - pandas
<3.0.0,>2.0.0 - pandera
<0.18,>=0.6.5 - retrying
<2,>=1.3.4 - scikit-learn
<2,>=1.2.0 - scikit-surprise
>=1.1.3 - seaborn
<1,>=0.13.0 - statsmodels
<=0.14.1 - transformers
<5,>=4.27.0
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- pipecat-aiAn open source framework for voice (and multimodal) assistantsSame primary category
- langflow-baseA Python package with a built-in web applicationSame primary category
- compressed-tensorsLibrary for utilization of compressed safetensors of neural network modelsSame primary category
- langgraphBuilding stateful, multi-actor applications with LLMsSame primary category
Related news
No published news event mentions this package or its repository yet.