RecTools

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RecTools is an easy-to-use Python library which makes the process of building recommender systems easier and faster than ever before.

✨ Highlights: HSTU model released! ✨

HSTU arhictecture from "Actions speak louder then words..." is now available in RecTools as HSTUModel:

• Fully compatible with our fit / recommend paradigm and require NO special data processing
• Supports context-aware recommendations in case Relative Time Bias is enabled
• Supports all loss options, item embedding options, category features utilization and other common modular functionality of RecTools transformer models
• In HSTU tutorial we show that original metrics reported for HSTU on public Movielens datasets may actually be underestimated
• Configurable, customizable, callback-friendly, checkpoints-included, logs-out-of-the-box, custom-validation-ready, multi-gpu-compatible! See Transformers Advanced Training User Guide and Transformers Customization Guide

✨ Highlights: RecTools framework at ACM RecSys'25 ✨

RecTools implementations are featured in ACM RecSys'25: "eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion":

• The article presents a systematic benchmark of Transformer modifications using RecTools models. It offers a detailed evaluation of training objectives, Transformer architectures, loss functions, and negative sampling strategies in realistic, production-like settings
• We introduce a new SOTA baseline, eSASRec, which combines SASRec’s training objective with LiGR Transformer layers and Sampled Softmax loss, forming a simple yet powerful recipe
• eSASRec shows 23% boost over SOTA models, such as ActionPiece, on academic benchmarks
• LiGR Transformer layers used in eSASRec are now in RecTools

Plase note that we always compare the quality of our implementations to academic papers results. Public benchmarks for transformer models SASRec and BERT4Rec show that RecTools implementations achieve highest scores on multiple datasets compared to other published results.

Get started

Prepare data with

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

import pandas as pd
    
from rectools import Columns
from rectools.dataset import Dataset
from rectools.models import SASRecModel

# 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 = SASRecModel(n_factors=64, epochs=100, loss="sampled_softmax")
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.
• catboost: adds CatBoost as a reranker for CandidateRankingModel

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.

• All of the models follow the same interface. No exceptions
• No need for manual creation of sparse matrixes, torch dataloaders 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 example
• 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.
• Our models can be initialized from configs and have useful methods like get_config, get_params, save, load. Common functions model_from_config, model_from_params and load_model are available. Check our example

Extended validation tools

calc_metrics for classification, ranking, "beyond-accuracy", DQ, popularity bias and between-model metrics

User guide | Documentation

DebiasConfig for debiased metrics calculation

User guide | Documentation

cross_validate for model metrics comparison

User guide

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 python3 and poetry 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]
• Andrey Semenov
• Mike Sokolov
• Maya Spirina
• Grigoriy Gusarov
• Aki Ariga
• Nikolay Undalov
• Aleksey Kuzin

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