Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/MobileTeleSystems/RecTools
RecTools - library to build Recommendation Systems easier and faster than ever before
https://github.com/MobileTeleSystems/RecTools
deep-learning machine-learning personalization recomendations recommendation-algorithms recommendation-engine recommendation-system recommender-system recsys
Last synced: 3 months ago
JSON representation
RecTools - library to build Recommendation Systems easier and faster than ever before
- Host: GitHub
- URL: https://github.com/MobileTeleSystems/RecTools
- Owner: MobileTeleSystems
- License: apache-2.0
- Created: 2022-07-13T09:24:15.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-30T10:56:09.000Z (3 months ago)
- Last Synced: 2024-07-30T12:42:00.182Z (3 months ago)
- Topics: deep-learning, machine-learning, personalization, recomendations, recommendation-algorithms, recommendation-engine, recommendation-system, recommender-system, recsys
- Language: Python
- Homepage:
- Size: 3.6 MB
- Stars: 263
- Watchers: 7
- Forks: 33
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
# RecTools
[](https://pypi.org/project/rectools)
[](https://pypi.org/project/rectools)
[](https://rectools.readthedocs.io)
[](https://github.com/MobileTeleSystems/RecTools/blob/main/LICENSE)
[](https://app.codecov.io/gh/MobileTeleSystems/RecTools)
[](https://github.com/MobileTeleSystems/RecTools/actions/workflows/test.yml?query=branch%3Amain++)
[](https://github.com/MobileTeleSystems/RecTools/graphs/contributors)
[](https://pepy.tech/project/rectools)
[](https://t.me/RecTools_Support)
Documentation |
Examples |
Tutorials |
Contributing |
Releases |
Developers Board
RecTools is an easy-to-use Python library which makes the process of building recommendation systems easier,
faster and more structured than ever before.
It includes built-in toolkits for data processing and metrics calculation,
a variety of recommender models, some wrappers for already existing implementations of popular algorithms
and model selection framework.
The aim is to collect ready-to-use solutions and best practices in one place to make processes
of creating your first MVP and deploying model to production as fast and easy as possible.
## Get started
Prepare data with
```shell
wget https://files.grouplens.org/datasets/movielens/ml-1m.zip
unzip ml-1m.zip
```
```python
import pandas as pd
from implicit.nearest_neighbours import TFIDFRecommender
from rectools import Columns
from rectools.dataset import Dataset
from rectools.models import ImplicitItemKNNWrapperModel
# Read the data
ratings = pd.read_csv(
"ml-1m/ratings.dat",
sep="::",
engine="python", # Because of 2-chars separators
header=None,
names=[Columns.User, Columns.Item, Columns.Weight, Columns.Datetime],
)
# Create dataset
dataset = Dataset.construct(ratings)
# Fit model
model = ImplicitItemKNNWrapperModel(TFIDFRecommender(K=10))
model.fit(dataset)
# Make recommendations
recos = model.recommend(
users=ratings[Columns.User].unique(),
dataset=dataset,
k=10,
filter_viewed=True,
)
```
## Installation
RecTools is on PyPI, so you can use `pip` to install it.
```
pip install rectools
```
The default version doesn't contain all the dependencies, because some of them are needed only for specific functionality. Available user extensions are the following:
- `lightfm`: adds wrapper for LightFM model,
- `torch`: adds models based on neural nets,
- `visuals`: adds visualization tools,
- `nmslib`: adds fast ANN recommenders.
Install extension:
```
pip install rectools[extension-name]
```
Install all extensions:
```
pip install rectools[all]
```
## Recommender Models
The table below lists recommender models that are available in RecTools.
See [recommender baselines extended tutorial](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/tutorials/baselines_extended_tutorial.ipynb) for deep dive into theory & practice of our supported models.
| Model | Type | Description (🎏 for user/item features, 🔆 for warm inference, ❄️ for cold inference support) | Tutorials & Benchmarks |
|----|----|---------|--------|
| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback.
🎏| 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS)
🚀 [50% boost to metrics with user & item features](examples/5_benchmark_iALS_with_features.ipynb) |
| [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |
| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses.
🎏 🔆 ❄️| 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM)
🚀 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |
| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |
| DSSM | Neural Network | `rectools.models.DSSMModel` - Two-tower Neural model that learns user and item embeddings utilising their explicit features and learning on triplet loss.
🎏 🔆 | - |
| Popular | Heuristic | `rectools.models.PopularModel` - Classic baseline which computes popularity of items and also accepts params like time window and type of popularity computation.
❄️| - |
| Popular in Category | Heuristic | `rectools.models.PopularInCategoryModel` - Model that computes poularity within category and applies mixing strategy to increase Diversity.
❄️| - |
| Random | Heuristic | `rectools.models.RandomModel` - Simple random algorithm useful to benchmark Novelty, Coverage, etc.
❄️| - |
- All of the models follow the same interface. **No exceptions**
- No need for manual creation of sparse matrixes or mapping ids. Preparing data for models is as simple as `dataset = Dataset.construct(interactions_df)`
- Fitting any model is as simple as `model.fit(dataset)`
- For getting recommendations `filter_viewed` and `items_to_recommend` options are available
- For item-to-item recommendations use `recommend_to_items` method
- For feeding user/item features to model just specify dataframes when constructing `Dataset`. [Check our tutorial](examples/4_dataset_with_features.ipynb)
- For warm / cold inference just provide all required ids in `users` or `target_items` parameters of `recommend` or `recommend_to_items` methods and make sure you have features in the dataset for warm users/items. **Nothing else is needed, everything works out of the box.**
## Contribution
[Contributing guide](CONTRIBUTING.rst)
To install all requirements
- you must have `python>=3.8` and `poetry>=1.5.0` installed
- make sure you have no active virtual environments (deactivate conda `base` if applicable)
- run
```
make install
```
For autoformatting run
```
make format
```
For linters check run
```
make lint
```
For tests run
```
make test
```
For coverage run
```
make coverage
```
To remove virtual environment run
```
make clean
```
## RecTools Team
- [Emiliy Feldman](https://github.com/feldlime) [Maintainer]
- [Daria Tikhonovich](https://github.com/blondered) [Maintainer]
- [Alexander Butenko](https://github.com/iomallach)
- [Andrey Semenov](https://github.com/In48semenov)
- [Mike Sokolov](https://github.com/mikesokolovv)
- [Maya Spirina](https://github.com/spirinamayya)
- [Grigoriy Gusarov](https://github.com/Gooogr)
Previous contributors: [Ildar Safilo](https://github.com/irsafilo) [ex-Maintainer], [Daniil Potapov](https://github.com/sharthZ23) [ex-Maintainer], [Igor Belkov](https://github.com/OzmundSedler), [Artem Senin](https://github.com/artemseninhse), [Mikhail Khasykov](https://github.com/mkhasykov), [Julia Karamnova](https://github.com/JuliaKup), [Maxim Lukin](https://github.com/groundmax), [Yuri Ulianov](https://github.com/yukeeul), [Egor Kratkov](https://github.com/jegorus), [Azat Sibagatulin](https://github.com/azatnv)