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 | 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 of the algorithms use OpenMP to support multi-threading. For OSX 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

If you want to utilize your GPUs, you might consider:

• TensorFlow installation instructions.
• PyTorch installation instructions.
• cuDNN (for Nvidia GPUs).

Getting started: your first Cornac experiment

Flow of an Experiment in Cornac

Load the built-in MovieLens 100K dataset (will be downloaded if not cached):

from cornac.datasets import movielens

ml_100k = movielens.load_feedback()

Split the data based on ratio:

from cornac.eval_methods import RatioSplit

ratio_split = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)

Here we are comparing Biased MF, PMF, and BPR:

from cornac.models import MF, PMF, BPR

mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)
pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lamda=0.001, seed=123)
bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)

Define metrics used to evaluate the models:

mae = cornac.metrics.MAE()
rmse = cornac.metrics.RMSE()
rec_20 = cornac.metrics.Recall(k=20)
ndcg_20 = cornac.metrics.NDCG(k=20)
auc = cornac.metrics.AUC()

Put everything together into an experiment and run it:

from cornac import Experiment

exp = Experiment(eval_method=ratio_split,
                 models=[mf, pmf, bpr],
                 metrics=[mae, rmse, rec_20, ndcg_20, auc],
                 user_based=True)
exp.run()

Output:

	MAE	RMSE	Recall@20	NDCG@20	AUC	Train (s)	Test (s)
MF	0.7431	0.9000	0.0654	0.0556	0.7485	0.0762	1.3927
PMF	0.7538	0.9143	0.0880	0.0719	0.7779	4.1340	1.2292
BPR	N/A	N/A	0.1449	0.1125	0.8715	2.3332	1.3235

For more details, please take a look at our examples.

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
2018	Collaborative Context Poisson Factorization (C2PF), paper	N/A	c2pf_exp.py
	Multi-Task Explainable Recommendation (MTER), paper	requirements.txt	mter_exp.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
	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
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
	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
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
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
	Global Average (GlobalAvg), paper	N/A	biased_mf.py
	Matrix Factorization (MF), paper	N/A	biased_mf.py, given_data.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
	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.

License

Apache License 2.0