cornac 1.12.0

A Comparative Framework for Multimodal Recommender Systems

Cornac

Cornac is a comparative framework for multimodal recommender systems. It focuses on making it convenient to work with models leveraging auxiliary data (e.g., item descriptive text and image, social network, etc). Cornac enables fast experiments and straightforward implementations of new models. It is highly compatible with existing machine learning libraries (e.g., TensorFlow, PyTorch).

Quick Links

Website | Documentation | Tutorials | Examples | Models | Datasets | Paper | Preferred.AI

Installation

Currently, we are supporting Python 3. There are several ways to install Cornac:

• From PyPI (you may need a C++ compiler):

  pip3 install cornac
  

• From Anaconda:

  conda install cornac -c conda-forge
  

• From the GitHub source (for latest updates):

  pip3 install Cython
  git clone https://github.com/PreferredAI/cornac.git
  cd cornac
  python3 setup.py install
  

Note:

Additional dependencies required by models are listed here.

Some algorithm implementations use OpenMP to support multi-threading. For Mac OS users, in order to run those algorithms efficiently, you might need to install gcc from Homebrew to have an OpenMP compiler:

brew install gcc | brew link gcc

Getting started: your first Cornac experiment

Flow of an Experiment in Cornac

import cornac
from cornac.eval_methods import RatioSplit
from cornac.models import MF, PMF, BPR
from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP

# load the built-in MovieLens 100K and split the data based on ratio
ml_100k = cornac.datasets.movielens.load_feedback()
rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)

# initialize models, here we are comparing: Biased MF, PMF, and BPR
models = [
    MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123),
    PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123),
    BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123),
]

# define metrics to evaluate the models
metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]

# put it together in an experiment, voilà!
cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run()

Output:

	MAE	RMSE	AUC	MAP	NDCG@10	Precision@10	Recall@10	Train (s)	Test (s)
MF	0.7430	0.8998	0.7445	0.0407	0.0479	0.0437	0.0352	0.13	1.57
PMF	0.7534	0.9138	0.7744	0.0491	0.0617	0.0533	0.0479	2.18	1.64
BPR	N/A	N/A	0.8695	0.0753	0.0975	0.0727	0.0891	3.74	1.49

For more details, please take a look at our examples as well as tutorials. For learning purposes, this list of tutorials on recommender systems will be more organized and comprehensive.

Models

The recommender models supported by Cornac are listed below. Why don't you join us to lengthen the list?

Year	Model and paper	Additional dependencies	Examples
2021	Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF), paper	requirements.txt	PreferredAI/bi-vae
	Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER), paper	N/A	PreferredAI/ComparER
2018	Collaborative Context Poisson Factorization (C2PF), paper	N/A	c2pf_exp.py
	Multi-Task Explainable Recommendation (MTER), paper	N/A	mter_exp.py
	Neural Attention Rating Regression with Review-level Explanations (NARRE), paper	requirements.txt	narre_example.py
	Probabilistic Collaborative Representation Learning (PCRL), paper	requirements.txt	pcrl_exp.py
	Variational Autoencoder for Collaborative Filtering (VAECF), paper	requirements.txt	vaecf_citeulike.py
2017	Collaborative Variational Autoencoder (CVAE), paper	requirements.txt	cvae_exp.py
	Conditional Variational Autoencoder for Collaborative Filtering (CVAECF), paper	requirements.txt	cvaecf_filmtrust.py
	Generalized Matrix Factorization (GMF), paper	requirements.txt	ncf_exp.py
	Indexable Bayesian Personalized Ranking (IBPR), paper	requirements.txt	ibpr_exp.py
	Matrix Co-Factorization (MCF), paper	N/A	mcf_office.py
	Multi-Layer Perceptron (MLP), paper	requirements.txt	ncf_exp.py
	Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF), paper	requirements.txt	ncf_exp.py
	Online Indexable Bayesian Personalized Ranking (Online IBPR), paper	requirements.txt
	Visual Matrix Factorization (VMF), paper	requirements.txt	vmf_clothing.py
2016	Collaborative Deep Ranking (CDR), paper	requirements.txt	cdr_exp.py
	Collaborative Ordinal Embedding (COE), paper	requirements.txt
	Convolutional Matrix Factorization (ConvMF), paper	requirements.txt	convmf_exp.py
	Spherical K-means (SKM), paper	N/A	skm_movielens.py
	Visual Bayesian Personalized Ranking (VBPR), paper	requirements.txt	vbpr_tradesy.py
2015	Collaborative Deep Learning (CDL), paper	requirements.txt	cdl_exp.py
	Hierarchical Poisson Factorization (HPF), paper	N/A	hpf_movielens.py
2014	Explicit Factor Model (EFM), paper	N/A	efm_exp.py
	Social Bayesian Personalized Ranking (SBPR), paper	N/A	sbpr_epinions.py
2013	Hidden Factors and Hidden Topics (HFT), paper	N/A	hft_exp.py
2012	Weighted Bayesian Personalized Ranking (WBPR), paper	N/A	bpr_netflix.py
2011	Collaborative Topic Modeling (CTR), paper	N/A	ctr_citeulike.py
Earlier	Baseline Only, paper	N/A	svd_exp.py
	Bayesian Personalized Ranking (BPR), paper	N/A	bpr_netflix.py
	Factorization Machines (FM), paper	Linux only	fm_example.py
	Global Average (GlobalAvg), paper	N/A	biased_mf.py
	Item K-Nearest-Neighbors (ItemKNN), paper	N/A	knn_movielens.py
	Matrix Factorization (MF), paper	N/A	biased_mf.py, given_data.py
	Maximum Margin Matrix Factorization (MMMF), paper	N/A	mmmf_exp.py
	Most Popular (MostPop), paper	N/A	bpr_netflix.py
	Non-negative Matrix Factorization (NMF), paper	N/A	nmf_exp.py
	Probabilistic Matrix Factorization (PMF), paper	N/A	pmf_ratio.py
	Singular Value Decomposition (SVD), paper	N/A	svd_exp.py
	Social Recommendation using PMF (SoRec), paper	N/A	sorec_filmtrust.py
	User K-Nearest-Neighbors (UserKNN), paper	N/A	knn_movielens.py
	Weighted Matrix Factorization (WMF), paper	requirements.txt	wmf_exp.py

Support

Your contributions at any level of the library are welcome. If you intend to contribute, please:

• Fork the Cornac repository to your own account.
• Make changes and create pull requests.

You can also post bug reports and feature requests in GitHub issues.

Citation

If you use Cornac in a scientific publication, we would appreciate citations to the following paper:

Cornac: A Comparative Framework for Multimodal Recommender Systems, Salah et al., JMLR 21, pp. 1-5, 2020.

Bibtex entry:

@article{cornac,
  author  = {Aghiles Salah and Quoc-Tuan Truong and Hady W. Lauw},
  title   = {Cornac: A Comparative Framework for Multimodal Recommender Systems},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {95},
  pages   = {1-5},
  url     = {http://jmlr.org/papers/v21/19-805.html}
}

License

Apache License 2.0