RecTools

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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

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,
)

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 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 ALS Wrapper	Matrix Factorization	rectools.models.ImplicitALSWrapperModel - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. 🎏	📙 Theory & Practice 🚀 50% boost to metrics with user & item features
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
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 🚀 10-25 times faster inference with RecTools
EASE	Linear Autoencoder	rectools.models.EASEModel - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix	📙 Theory & Practice
PureSVD	Matrix Factorization	rectools.models.PureSVDModel - Truncated Singular Value Decomposition of user-item interactions matrix	📙 Theory & Practice
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
• 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.

Extended validation tools

DebiasConfig for debiased metrics calculation

User guide | Documentation

VisualApp for model recommendations comparison

Example | Demo | Documentation

MetricsApp for metrics trade-off analysis

Example | Documentation

Contribution

Contributing guide

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 [Maintainer]
• Daria Tikhonovich [Maintainer]
• Alexander Butenko
• Andrey Semenov
• Mike Sokolov
• Maya Spirina
• Grigoriy Gusarov

Previous contributors: Ildar Safilo [ex-Maintainer], Daniil Potapov [ex-Maintainer], Igor Belkov, Artem Senin, Mikhail Khasykov, Julia Karamnova, Maxim Lukin, Yuri Ulianov, Egor Kratkov, Azat Sibagatulin