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).
Cornac is one of the frameworks recommended by ACM RecSys 2023 for the evaluation and reproducibility of recommendation algorithms.
Quick Links
Website |
Documentation |
Tutorials |
Examples |
Models |
Datasets |
Paper |
Preferred.AI
![License](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)
Installation
Currently, we are supporting Python 3. There are several ways to install Cornac:
-
From PyPI (recommended):
pip3 install cornac
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From Anaconda:
conda install cornac -c conda-forge
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From the GitHub source (for latest updates):
pip3 install Cython numpy scipy
pip3 install git+https://github.com/PreferredAI/cornac.git
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.jpg)
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
ml_100k = cornac.datasets.movielens.load_feedback()
rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)
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, lambda_reg=0.001, seed=123)
bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)
models = [mf, pmf, bpr]
metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]
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 | 0.0548 | 0.0761 | 0.0675 | 0.0463 | 0.13 | 1.57 |
PMF | 0.7534 | 0.9138 | 0.7744 | 0.0671 | 0.0969 | 0.0813 | 0.0639 | 2.18 | 1.64 |
BPR | N/A | N/A | 0.8695 | 0.1042 | 0.1500 | 0.1110 | 0.1195 | 3.74 | 1.49 |
Model serving
Here, we provide a simple way to serve a Cornac model by launching a standalone web service with Flask. It is very handy for testing or creating a demo application. First, we install the dependency:
$ pip3 install Flask
Supposed that we want to serve the trained BPR model from previous example, we need to save it:
bpr.save("save_dir", save_trainset=True)
After that, the model can be deployed easily by running Cornac serving app as follows:
$ FLASK_APP='cornac.serving.app' \
MODEL_PATH='save_dir/BPR' \
MODEL_CLASS='cornac.models.BPR' \
flask run --host localhost --port 8080
Here we go, our model service is now ready. Let's get top-5
item recommendations for the user "63"
:
$ curl -X GET "http://localhost:8080/recommend?uid=63&k=5&remove_seen=false"
If we want to remove seen items during training, we need to provide TRAIN_SET
which has been saved with the model earlier, when starting the serving app. We can also leverage WSGI server for model deployment in production. Please refer to this guide for more details.
Efficient retrieval with ANN search
One important aspect of deploying recommender model is efficient retrieval via Approximate Nearest Neighbor (ANN) search in vector space. Cornac integrates several vector similarity search frameworks for the ease of deployment. This example demonstrates how ANN search will work seamlessly with any recommender models supporting it (e.g., matrix factorization).
Models
The table below lists the recommendation models/algorithms featured in Cornac. Examples are provided as quick-start showcasing an easy to run script, or as deep-dive explaining the math and intuition behind each model. Why don't you join us to lengthen the list?
Year | Model and Paper | Type | Environment | Example |
---|
2024 | Hypergraphs with Attention on Reviews (HypAR), docs, paper | Hybrid / Sentiment / Explainable | requirements, CPU / GPU | quick-start |
2022 | Disentangled Multimodal Representation Learning for Recommendation (DMRL), docs, paper | Content-Based / Text & Image | requirements, CPU / GPU | quick-start |
2021 | Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF), docs, paper | Collaborative Filtering / Content-Based | requirements, CPU / GPU | quick-start, deep-dive |
| Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec), docs, paper | Content-Based / Image | requirements, CPU / GPU | quick-start |
| Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER), docs, paper | Explainable | CPU | quick-start |
2020 | Adversarial Multimedia Recommendation (AMR), docs, paper | Content-Based / Image | requirements, CPU / GPU | quick-start |
| Hybrid Deep Representation Learning of Ratings and Reviews (HRDR), docs, paper | Content-Based / Text | requirements, CPU / GPU | quick-start |
| LightGCN: Simplifying and Powering Graph Convolution Network, docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start |
| Predicting Temporal Sets with Deep Neural Networks (DNNTSP), docs, paper | Next-Basket | requirements, CPU / GPU | quick-start |
| Recency Aware Collaborative Filtering (UPCF), docs, paper | Next-Basket | requirements, CPU | quick-start |
| Temporal-Item-Frequency-based User-KNN (TIFUKNN), docs, paper | Next-Basket | CPU | quick-start |
| Variational Autoencoder for Top-N Recommendations (RecVAE), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start |
2019 | Correlation-Sensitive Next-Basket Recommendation (Beacon), docs, paper | Next-Basket | requirements, CPU / GPU | quick-start |
| Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ), docs, paper | Collaborative Filtering | CPU | quick-start |
| Neural Graph Collaborative Filtering (NGCF), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start |
2018 | Collaborative Context Poisson Factorization (C2PF), docs, paper | Content-Based / Graph | CPU | quick-start |
| Graph Convolutional Matrix Completion (GCMC), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start |
| Multi-Task Explainable Recommendation (MTER), docs, paper | Explainable | CPU | quick-start, deep-dive |
| Neural Attention Rating Regression with Review-level Explanations (NARRE), docs, paper | Explainable / Content-Based | requirements, CPU / GPU | quick-start |
| Probabilistic Collaborative Representation Learning (PCRL), docs, paper | Content-Based / Graph | requirements, CPU / GPU | quick-start |
| Variational Autoencoder for Collaborative Filtering (VAECF), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, param-search, deep-dive |
2017 | Collaborative Variational Autoencoder (CVAE), docs, paper | Content-Based / Text | requirements, CPU / GPU | quick-start |
| Conditional Variational Autoencoder for Collaborative Filtering (CVAECF), docs, paper | Content-Based / Text | requirements, CPU / GPU | quick-start |
| Generalized Matrix Factorization (GMF), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, deep-dive |
| Indexable Bayesian Personalized Ranking (IBPR), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, deep-dive |
| Matrix Co-Factorization (MCF), docs, paper | Content-Based / Graph | CPU | quick-start, cross-modality |
| Multi-Layer Perceptron (MLP), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, deep-dive |
| Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, deep-dive |
| Online Indexable Bayesian Personalized Ranking (Online IBPR), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, deep-dive |
| Visual Matrix Factorization (VMF), docs, paper | Content-Based / Image | requirements, CPU / GPU | quick-start |
2016 | Collaborative Deep Ranking (CDR), docs, paper | Content-Based / Text | requirements, CPU / GPU | quick-start |
| Collaborative Ordinal Embedding (COE), docs, paper | Collaborative Filtering | requirements, CPU / GPU | |
| Convolutional Matrix Factorization (ConvMF), docs, paper | Content-Based / Text | requirements, CPU / GPU | quick-start, deep-dive |
| Learning to Rank Features for Recommendation over Multiple Categories (LRPPM), docs, paper | Explainable | CPU | quick-start |
| Session-based Recommendations With Recurrent Neural Networks (GRU4Rec), docs, paper | Next-Item | requirements, CPU / GPU | quick-start |
| Spherical K-means (SKM), docs, paper | Collaborative Filtering | CPU | quick-start |
| Visual Bayesian Personalized Ranking (VBPR), docs, paper | Content-Based / Image | requirements, CPU / GPU | quick-start, cross-modality, deep-dive |
2015 | Collaborative Deep Learning (CDL), docs, paper | Content-Based / Text | requirements, CPU / GPU | quick-start, deep-dive |
| Hierarchical Poisson Factorization (HPF), docs, paper | Collaborative Filtering | CPU | quick-start |
| TriRank: Review-aware Explainable Recommendation by Modeling Aspects, docs, paper | Explainable | CPU | quick-start |
2014 | Explicit Factor Model (EFM), docs, paper | Explainable | CPU | quick-start, deep-dive |
| Social Bayesian Personalized Ranking (SBPR), docs, paper | Content-Based / Social | CPU | quick-start |
2013 | Hidden Factors and Hidden Topics (HFT), docs, paper | Content-Based / Text | CPU | quick-start |
2012 | Weighted Bayesian Personalized Ranking (WBPR), docs, paper | Collaborative Filtering | CPU | quick-start |
2011 | Collaborative Topic Regression (CTR), docs, paper | Content-Based / Text | CPU | quick-start, deep-dive |
Earlier | Baseline Only, docs, paper | Baseline | CPU | quick-start |
| Bayesian Personalized Ranking (BPR), docs paper | Collaborative Filtering | CPU | quick-start, deep-dive |
| Factorization Machines (FM), docs, paper | Collaborative Filtering / Content-Based | Linux, CPU | quick-start, deep-dive |
| Global Average (GlobalAvg), docs, paper | Baseline | CPU | quick-start |
| Global Personalized Top Frequent (GPTop), paper | Next-Basket | CPU | quick-start |
| Item K-Nearest-Neighbors (ItemKNN), docs, paper | Neighborhood-Based | CPU | quick-start, deep-dive |
| Matrix Factorization (MF), docs, paper | Collaborative Filtering | CPU / GPU | quick-start, pre-split-data, deep-dive |
| Maximum Margin Matrix Factorization (MMMF), docs, paper | Collaborative Filtering | CPU | quick-start |
| Most Popular (MostPop), docs, paper | Baseline | CPU | quick-start |
| Non-negative Matrix Factorization (NMF), docs, paper | Collaborative Filtering | CPU | quick-start, deep-dive |
| Probabilistic Matrix Factorization (PMF), docs, paper | Collaborative Filtering | CPU | quick-start |
| Session Popular (SPop), docs, paper | Next-Item / Baseline | CPU | quick-start |
| Singular Value Decomposition (SVD), docs, paper | Collaborative Filtering | CPU | quick-start, deep-dive |
| Social Recommendation using PMF (SoRec), docs, paper | Content-Based / Social | CPU | quick-start, deep-dive |
| User K-Nearest-Neighbors (UserKNN), docs, paper | Neighborhood-Based | CPU | quick-start, deep-dive |
| Weighted Matrix Factorization (WMF), docs, paper | Collaborative Filtering | requirements, CPU / GPU | quick-start, deep-dive |
Resources
Contributing
This project welcomes contributions and suggestions. Before contributing, please see our contribution guidelines.
Citation
If you use Cornac in a scientific publication, we would appreciate citations to the following papers:
Cornac: A Comparative Framework for Multimodal Recommender Systems, Salah et al., Journal of Machine Learning Research, 21(95):1–5, 2020.
@article{salah2020cornac,
title={Cornac: A Comparative Framework for Multimodal Recommender Systems},
author={Salah, Aghiles and Truong, Quoc-Tuan and Lauw, Hady W},
journal={Journal of Machine Learning Research},
volume={21},
number={95},
pages={1--5},
year={2020}
}
Exploring Cross-Modality Utilization in Recommender Systems, Truong et al., IEEE Internet Computing, 25(4):50–57, 2021.
@article{truong2021exploring,
title={Exploring Cross-Modality Utilization in Recommender Systems},
author={Truong, Quoc-Tuan and Salah, Aghiles and Tran, Thanh-Binh and Guo, Jingyao and Lauw, Hady W},
journal={IEEE Internet Computing},
year={2021},
publisher={IEEE}
}
Multi-Modal Recommender Systems: Hands-On Exploration, Truong et al., ACM Conference on Recommender Systems, 2021.
@inproceedings{truong2021multi,
title={Multi-modal recommender systems: Hands-on exploration},
author={Truong, Quoc-Tuan and Salah, Aghiles and Lauw, Hady},
booktitle={Fifteenth ACM Conference on Recommender Systems},
pages={834--837},
year={2021}
}
License
Apache License 2.0