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Collie is a library for preparing, training, and evaluating implicit deep learning hybrid recommender systems, named after the Border Collie dog breed.

Collie offers a collection of simple APIs for preparing and splitting datasets, incorporating item metadata directly into a model architecture or loss, efficiently evaluating a model's performance on the GPU, and so much more. Above all else though, Collie is built with flexibility and customization in mind, allowing for faster prototyping and experimentation.

See the documentation for more details.

"We adopted 2 Border Collies a year ago and they are about 3 years old. They are completely obsessed with fetch and tennis balls and it's getting out of hand. They live in the fenced back yard and when anyone goes out there they instantly run around frantically looking for a tennis ball. If there is no ball they will just keep looking and will not let you pet them. When you do have a ball, they are 100% focused on it and will not notice anything else going on around them, like it's their whole world."

-- A Reddit thread on r/DogTraining


pip install collie_recs

Quick Start

Open In Colab

Creating and evaluating an implicit matrix factorization model with MovieLens 100K data is simple with Collie:

from collie_recs.cross_validation import stratified_split
from collie_recs.interactions import Interactions
from collie_recs.metrics import auc, evaluate_in_batches, mapk, mrr
from collie_recs.model import MatrixFactorizationModel, CollieTrainer
from collie_recs.movielens import read_movielens_df
from collie_recs.utils import convert_to_implicit

# read in MovieLens 100K data
df = read_movielens_df()

# convert the data to implicit
df_imp = convert_to_implicit(df)

# store data as ``Interactions``
interactions = Interactions(users=df_imp['user_id'],

# perform a data split
train, val = stratified_split(interactions)

# train an implicit ``MatrixFactorization`` model
model = MatrixFactorizationModel(train=train,
trainer = CollieTrainer(model, max_epochs=10)

# evaluate the model
auc_score, mrr_score, mapk_score = evaluate_in_batches([auc, mrr, mapk], val, model)

print(f'AUC:          {auc_score}')
print(f'MRR:          {mrr_score}')
print(f'MAP@10:       {mapk_score}')

More complicated examples of pipelines can be viewed for MovieLens 100K data here, in notebooks here, and documentation here.

Comparison With Other Open-Source Recommendation Libraries

On some smaller screens, you might have to scroll right to see the full table. ➡️

Aspect Included in Library Surprise LightFM FastAI Spotlight RecBole TensorFlow Recommenders Collie
Implicit data support for when we only know when a user interacts with an item or not, not the explicit rating the user gave the item
Explicit data support for when we know the explicit rating the user gave the item *
Support for side-data incorporated directly into the models
Support a flexible framework for new model architectures and experimentation
Deep learning libraries utilizing speed-ups with a GPU and able to implement new, cutting-edge deep learning algorithms
Automatic support for multi-GPU training
Actively supported and maintained
Type annotations for classes, methods, and functions
Scalable for larger, out-of-memory datasets
Includes model zoo with two or more model architectures implemented
Includes implicit loss functions for training and metric functions for model evaluation
Includes adaptive loss functions for multiple negative examples
Includes loss functions that account for side-data

* Coming soon!

The following table notes shows the results of an experiment training and evaluating recommendation models in some popular implicit recommendation model frameworks on a common MovieLens 10M dataset. The data was split via a 90/5/5 stratified data split. Each model was trained for a maximum of 40 epochs using an embedding dimension of 32. For each model, we used default hyperparameters (unless otherwise noted below).

Model MAP@10 Score Notes
Randomly initialized, untrained model 0.0001
Logistic MF 0.0128 Using the CUDA implementation.
LightFM with BPR Loss 0.0180
ALS 0.0189 Using the CUDA implementation.
BPR 0.0301 Using the CUDA implementation.
Spotlight 0.0376 Using adaptive hinge loss.
LightFM with WARP Loss 0.0412
Collie MatrixFactorizationModel 0.0425 Using a separate SGD bias optimizer.

At ShopRunner, we have found Collie models outperform comparable LightFM models with up to 64% improved MAP@10 scores.


To run locally, begin by creating a data path environment variable:

# Define where on your local hard drive you want to store data. It is best if this
# location is not inside the repo itself. An example is below
export DATA_PATH=$HOME/data/collie_recs

Run development from within the Docker container:

docker build -t collie_recs .

# run the container in interactive mode, leaving port ``8888`` open for Jupyter
docker run \
    -it \
    --rm \
    -v "${DATA_PATH}:/data" \
    -v "${PWD}:/collie_recs" \
    -p 8888:8888 \
    collie_recs /bin/bash

Run on a GPU:

docker build -t collie_recs .

# run the container in interactive mode, leaving port ``8888`` open for Jupyter
docker run \
    -it \
    --rm \
    --gpus all \
    -v "${DATA_PATH}:/data" \
    -v "${PWD}:/collie_recs" \
    -p 8888:8888 \
    collie_recs /bin/bash

Start JupyterLab

To run JupyterLab, start the container and execute the following:

jupyter lab --ip --no-browser --allow-root

Connect to JupyterLab here: http://localhost:8888/lab

Unit Tests

Library unit tests in this repo are to be run in the Docker container:

# execute unit tests
pytest --cov-report term --cov=collie_recs

Note that a handful of tests require the MovieLens 100K dataset to be downloaded (~5MB in size), meaning that either before or during test time, there will need to be an internet connection. This dataset only needs to be downloaded a single time for use in both unit tests and tutorials.


The Collie library supports Read the Docs documentation. To compile locally,

cd docs
make html

# open local docs
open build/html/index.html


A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.





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