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.

For more details, see the Documentation and Tutorials.

Prepare data with

wget https://files.grouplens.org/datasets/movielens/ml-1m.zip
unzip ml-1m.zip

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,
)

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 models. Available user extensions are the following:

• lightfm: adds wrapper for LightFM model,
• torch: adds models based on neural nets,
• nmslib: adds fast ANN recommenders.

Install extension:

pip install rectools[extension-name]

Install all extensions:

pip install rectools[all]

Important: If you're using poetry and you want to add rectools to your project, then you should either install rectools without lightfm extras or use poetry==1.4.0 and add to your poetry.toml file the next lines:

[experimental]
new-installer = false

The table below lists recommender models that are available in RecTools.

• 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

To install all requirements run

make install

You must have python3 and poetry==1.4.0 installed.

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

• Emiliy Feldman
• Ildar Safilo
• Daniil Potapov
• Igor Belkov
• Artem Senin
• Alexander Butenko
• Mikhail Khasykov
• Daria Tikhonovich