path: root/python-cornac.spec

%global _empty_manifest_terminate_build 0
Name:		python-cornac
Version:	1.15.0
Release:	1
Summary:	A Comparative Framework for Multimodal Recommender Systems
License:	Apache Software License
URL:		https://cornac.preferred.ai
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/37/00/f4868b87425922afaa60ea8bc16613d1330bb7bddd67faaaba67a83264e8/cornac-1.15.0.tar.gz

Requires:	python3-numpy
Requires:	python3-scipy
Requires:	python3-tqdm
Requires:	python3-powerlaw
Requires:	python3-pytest
Requires:	python3-pytest-pep8
Requires:	python3-pytest-xdist
Requires:	python3-pytest-cov

%description
# 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](https://cornac.preferred.ai/) |
[Documentation](https://cornac.readthedocs.io/en/latest/index.html) |
[Tutorials](tutorials#tutorials) |
[Examples](https://github.com/PreferredAI/cornac/tree/master/examples#cornac-examples-directory) |
[Models](#models) |
[Datasets](./cornac/datasets/README.md#datasets) |
[Paper](http://www.jmlr.org/papers/volume21/19-805/19-805.pdf) |
[Preferred.AI](https://preferred.ai/)

[![.github/workflows/python-package.yml](https://github.com/PreferredAI/cornac/actions/workflows/python-package.yml/badge.svg)](https://github.com/PreferredAI/cornac/actions/workflows/python-package.yml)
[![CircleCI](https://img.shields.io/circleci/project/github/PreferredAI/cornac/master.svg?logo=circleci)](https://circleci.com/gh/PreferredAI/cornac)
[![AppVeyor](https://ci.appveyor.com/api/projects/status/0yq4td1xg4kkhdwu?svg=true)](https://ci.appveyor.com/project/tqtg/cornac)
[![Codecov](https://img.shields.io/codecov/c/github/PreferredAI/cornac/master.svg?logo=codecov)](https://codecov.io/gh/PreferredAI/cornac)
[![Docs](https://img.shields.io/readthedocs/cornac/latest.svg)](https://cornac.readthedocs.io/en/latest)
<br />
[![Release](https://img.shields.io/github/release-pre/PreferredAI/cornac.svg)](https://github.com/PreferredAI/cornac/releases)
[![PyPI](https://img.shields.io/pypi/v/cornac.svg)](https://pypi.org/project/cornac/)
[![Conda](https://img.shields.io/conda/vn/conda-forge/cornac.svg)](https://anaconda.org/conda-forge/cornac)
[![Conda Recipe](https://img.shields.io/badge/recipe-cornac-green.svg)](https://github.com/conda-forge/cornac-feedstock)
[![Downloads](https://static.pepy.tech/personalized-badge/cornac?period=total&units=international_system&left_color=grey&right_color=orange&left_text=downloads)](https://pepy.tech/project/cornac)
<br />
[![Python](https://img.shields.io/pypi/pyversions/cornac.svg)](https://cornac.preferred.ai/)
[![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/cornac.svg)](https://anaconda.org/conda-forge/cornac)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)](https://opensource.org/licenses/Apache-2.0)


## Installation

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

- **From PyPI (you may need a C++ compiler):**
  ```bash
  pip3 install cornac
  ```

- **From Anaconda:**
  ```bash
  conda install cornac -c conda-forge
  ```

- **From the GitHub source (for latest updates):**
  ```bash
  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](README.md#Models).

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:
```bash
brew install gcc | brew link gcc
```

## Getting started: your first Cornac experiment

![](flow.jpg)
<p align="center"><i>Flow of an Experiment in Cornac</i></p>

```python
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](cornac/models/mf)   | 0.7430 | 0.8998 | 0.7445 |  0.0407 |  0.0479 |       0.0437 |    0.0352 |       0.13 |     1.57 |
| [PMF](cornac/models/pmf) | 0.7534 | 0.9138 | 0.7744 |  0.0491 |  0.0617 |       0.0533 |    0.0479 |       2.18 |     1.64 |
| [BPR](cornac/models/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](examples) as well as [tutorials](tutorials). For learning purposes, this list of [tutorials on recommender systems](https://github.com/PreferredAI/tutorials/tree/master/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)](cornac/models/bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | [requirements.txt](cornac/models/bivaecf/requirements.txt) | [PreferredAI/bi-vae](https://github.com/PreferredAI/bi-vae)
|      | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | [requirements.txt](cornac/models/causalrec/requirements.txt) | [causalrec_clothing.py](examples/causalrec_clothing.py)
|      | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | N/A | [PreferredAI/ComparER](https://github.com/PreferredAI/ComparER)
| 2020 | [Adversarial Training Towards Robust Multimedia Recommender System (AMR)](cornac/models/amr), [paper](https://ieeexplore.ieee.org/document/8618394) | [requirements.txt](cornac/models/amr/requirements.txt) | [amr_clothing.py](examples/amr_clothing.py)
| 2019 | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | N/A | [ease_movielens.py](examples/ease_movielens.py)
| 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | N/A | [c2pf_exp.py](examples/c2pf_example.py)
|      | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | N/A | [mter_exp.py](examples/mter_example.py)
|      | [Neural Attention Rating Regression with Review-level Explanations (NARRE)](cornac/models/narre), [paper](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf) | [requirements.txt](cornac/models/narre/requirements.txt) | [narre_example.py](examples/narre_example.py)
|      | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | [requirements.txt](cornac/models/pcrl/requirements.txt) | [pcrl_exp.py](examples/pcrl_example.py)
|      | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | [requirements.txt](cornac/models/vaecf/requirements.txt) | [vaecf_citeulike.py](examples/vaecf_citeulike.py)
| 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | [requirements.txt](cornac/models/cvae/requirements.txt) | [cvae_exp.py](examples/cvae_example.py)
|      | [Conditional Variational Autoencoder for Collaborative Filtering (CVAECF)](cornac/models/cvaecf), [paper](https://seslab.kaist.ac.kr/xe2/?module=file&act=procFileDownload&file_srl=18019&sid=4be19b9d0134a4aeacb9ef1ecd81c784&module_srl=1379) | [requirements.txt](cornac/models/cvaecf/requirements.txt) | [cvaecf_filmtrust.py](examples/cvaecf_filmtrust.py)
|      | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/ibpr/requirements.txt) | [ibpr_exp.py](examples/ibpr_example.py)
|      | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | N/A | [mcf_office.py](examples/mcf_office.py)
|      | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/online_ibpr/requirements.txt) |
|      | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | [requirements.txt](cornac/models/vmf/requirements.txt) | [vmf_clothing.py](examples/vmf_clothing.py)
| 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | [requirements.txt](cornac/models/cdr/requirements.txt) | [cdr_exp.py](examples/cdr_example.py)
|      | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | [requirements.txt](cornac/models/coe/requirements.txt) |
|      | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | [requirements.txt](cornac/models/conv_mf/requirements.txt) | [convmf_exp.py](examples/conv_mf_example.py)
|      | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | N/A | [skm_movielens.py](examples/skm_movielens.py)
|      | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | [requirements.txt](cornac/models/vbpr/requirements.txt) | [vbpr_tradesy.py](examples/vbpr_tradesy.py)
| 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | [requirements.txt](cornac/models/cdl/requirements.txt) | [cdl_exp.py](examples/cdl_example.py)
|      | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A | [hpf_movielens.py](examples/hpf_movielens.py)
| 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](http://yongfeng.me/attach/efm-zhang.pdf) | N/A | [efm_exp.py](examples/efm_example.py)
|      | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | N/A | [sbpr_epinions.py](examples/sbpr_epinions.py)
| 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | N/A | [hft_exp.py](examples/hft_example.py)
| 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
| 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | N/A | [ctr_citeulike.py](examples/ctr_example_citeulike.py)
| Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
|      | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
|      | [Factorization Machines (FM)](cornac/models/fm), [paper](https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf) | Linux only | [fm_example.py](examples/fm_example.py)
|      | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py)
|      | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | N/A | [knn_movielens.py](examples/knn_movielens.py)
|      | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py), [given_data.py](examples/given_data.py)
|      | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | N/A | [mmmf_exp.py](examples/mmmf_exp.py)
|      | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
|      | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | N/A | [nmf_exp.py](examples/nmf_example.py)
|      | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | N/A | [pmf_ratio.py](examples/pmf_ratio.py)
|      | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
|      | [Social Recommendation using PMF (SoRec)](cornac/models/sorec), [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) | N/A | [sorec_filmtrust.py](examples/sorec_filmtrust.py)
|      | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | N/A | [knn_movielens.py](examples/knn_movielens.py)
|      | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | [requirements.txt](cornac/models/wmf/requirements.txt) | [wmf_exp.py](examples/wmf_example.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](https://github.com/PreferredAI/cornac/issues).

## 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](http://jmlr.org/papers/v21/19-805.html), 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](https://ieeexplore.ieee.org/abstract/document/9354572), 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](https://dl.acm.org/doi/10.1145/3460231.3473324), 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](LICENSE)




%package -n python3-cornac
Summary:	A Comparative Framework for Multimodal Recommender Systems
Provides:	python-cornac
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-cornac
# 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](https://cornac.preferred.ai/) |
[Documentation](https://cornac.readthedocs.io/en/latest/index.html) |
[Tutorials](tutorials#tutorials) |
[Examples](https://github.com/PreferredAI/cornac/tree/master/examples#cornac-examples-directory) |
[Models](#models) |
[Datasets](./cornac/datasets/README.md#datasets) |
[Paper](http://www.jmlr.org/papers/volume21/19-805/19-805.pdf) |
[Preferred.AI](https://preferred.ai/)

[![.github/workflows/python-package.yml](https://github.com/PreferredAI/cornac/actions/workflows/python-package.yml/badge.svg)](https://github.com/PreferredAI/cornac/actions/workflows/python-package.yml)
[![CircleCI](https://img.shields.io/circleci/project/github/PreferredAI/cornac/master.svg?logo=circleci)](https://circleci.com/gh/PreferredAI/cornac)
[![AppVeyor](https://ci.appveyor.com/api/projects/status/0yq4td1xg4kkhdwu?svg=true)](https://ci.appveyor.com/project/tqtg/cornac)
[![Codecov](https://img.shields.io/codecov/c/github/PreferredAI/cornac/master.svg?logo=codecov)](https://codecov.io/gh/PreferredAI/cornac)
[![Docs](https://img.shields.io/readthedocs/cornac/latest.svg)](https://cornac.readthedocs.io/en/latest)
<br />
[![Release](https://img.shields.io/github/release-pre/PreferredAI/cornac.svg)](https://github.com/PreferredAI/cornac/releases)
[![PyPI](https://img.shields.io/pypi/v/cornac.svg)](https://pypi.org/project/cornac/)
[![Conda](https://img.shields.io/conda/vn/conda-forge/cornac.svg)](https://anaconda.org/conda-forge/cornac)
[![Conda Recipe](https://img.shields.io/badge/recipe-cornac-green.svg)](https://github.com/conda-forge/cornac-feedstock)
[![Downloads](https://static.pepy.tech/personalized-badge/cornac?period=total&units=international_system&left_color=grey&right_color=orange&left_text=downloads)](https://pepy.tech/project/cornac)
<br />
[![Python](https://img.shields.io/pypi/pyversions/cornac.svg)](https://cornac.preferred.ai/)
[![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/cornac.svg)](https://anaconda.org/conda-forge/cornac)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)](https://opensource.org/licenses/Apache-2.0)


## Installation

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

- **From PyPI (you may need a C++ compiler):**
  ```bash
  pip3 install cornac
  ```

- **From Anaconda:**
  ```bash
  conda install cornac -c conda-forge
  ```

- **From the GitHub source (for latest updates):**
  ```bash
  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](README.md#Models).

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:
```bash
brew install gcc | brew link gcc
```

## Getting started: your first Cornac experiment

![](flow.jpg)
<p align="center"><i>Flow of an Experiment in Cornac</i></p>

```python
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](cornac/models/mf)   | 0.7430 | 0.8998 | 0.7445 |  0.0407 |  0.0479 |       0.0437 |    0.0352 |       0.13 |     1.57 |
| [PMF](cornac/models/pmf) | 0.7534 | 0.9138 | 0.7744 |  0.0491 |  0.0617 |       0.0533 |    0.0479 |       2.18 |     1.64 |
| [BPR](cornac/models/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](examples) as well as [tutorials](tutorials). For learning purposes, this list of [tutorials on recommender systems](https://github.com/PreferredAI/tutorials/tree/master/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)](cornac/models/bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | [requirements.txt](cornac/models/bivaecf/requirements.txt) | [PreferredAI/bi-vae](https://github.com/PreferredAI/bi-vae)
|      | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | [requirements.txt](cornac/models/causalrec/requirements.txt) | [causalrec_clothing.py](examples/causalrec_clothing.py)
|      | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | N/A | [PreferredAI/ComparER](https://github.com/PreferredAI/ComparER)
| 2020 | [Adversarial Training Towards Robust Multimedia Recommender System (AMR)](cornac/models/amr), [paper](https://ieeexplore.ieee.org/document/8618394) | [requirements.txt](cornac/models/amr/requirements.txt) | [amr_clothing.py](examples/amr_clothing.py)
| 2019 | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | N/A | [ease_movielens.py](examples/ease_movielens.py)
| 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | N/A | [c2pf_exp.py](examples/c2pf_example.py)
|      | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | N/A | [mter_exp.py](examples/mter_example.py)
|      | [Neural Attention Rating Regression with Review-level Explanations (NARRE)](cornac/models/narre), [paper](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf) | [requirements.txt](cornac/models/narre/requirements.txt) | [narre_example.py](examples/narre_example.py)
|      | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | [requirements.txt](cornac/models/pcrl/requirements.txt) | [pcrl_exp.py](examples/pcrl_example.py)
|      | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | [requirements.txt](cornac/models/vaecf/requirements.txt) | [vaecf_citeulike.py](examples/vaecf_citeulike.py)
| 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | [requirements.txt](cornac/models/cvae/requirements.txt) | [cvae_exp.py](examples/cvae_example.py)
|      | [Conditional Variational Autoencoder for Collaborative Filtering (CVAECF)](cornac/models/cvaecf), [paper](https://seslab.kaist.ac.kr/xe2/?module=file&act=procFileDownload&file_srl=18019&sid=4be19b9d0134a4aeacb9ef1ecd81c784&module_srl=1379) | [requirements.txt](cornac/models/cvaecf/requirements.txt) | [cvaecf_filmtrust.py](examples/cvaecf_filmtrust.py)
|      | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/ibpr/requirements.txt) | [ibpr_exp.py](examples/ibpr_example.py)
|      | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | N/A | [mcf_office.py](examples/mcf_office.py)
|      | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/online_ibpr/requirements.txt) |
|      | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | [requirements.txt](cornac/models/vmf/requirements.txt) | [vmf_clothing.py](examples/vmf_clothing.py)
| 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | [requirements.txt](cornac/models/cdr/requirements.txt) | [cdr_exp.py](examples/cdr_example.py)
|      | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | [requirements.txt](cornac/models/coe/requirements.txt) |
|      | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | [requirements.txt](cornac/models/conv_mf/requirements.txt) | [convmf_exp.py](examples/conv_mf_example.py)
|      | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | N/A | [skm_movielens.py](examples/skm_movielens.py)
|      | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | [requirements.txt](cornac/models/vbpr/requirements.txt) | [vbpr_tradesy.py](examples/vbpr_tradesy.py)
| 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | [requirements.txt](cornac/models/cdl/requirements.txt) | [cdl_exp.py](examples/cdl_example.py)
|      | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A | [hpf_movielens.py](examples/hpf_movielens.py)
| 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](http://yongfeng.me/attach/efm-zhang.pdf) | N/A | [efm_exp.py](examples/efm_example.py)
|      | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | N/A | [sbpr_epinions.py](examples/sbpr_epinions.py)
| 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | N/A | [hft_exp.py](examples/hft_example.py)
| 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
| 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | N/A | [ctr_citeulike.py](examples/ctr_example_citeulike.py)
| Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
|      | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
|      | [Factorization Machines (FM)](cornac/models/fm), [paper](https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf) | Linux only | [fm_example.py](examples/fm_example.py)
|      | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py)
|      | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | N/A | [knn_movielens.py](examples/knn_movielens.py)
|      | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py), [given_data.py](examples/given_data.py)
|      | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | N/A | [mmmf_exp.py](examples/mmmf_exp.py)
|      | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
|      | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | N/A | [nmf_exp.py](examples/nmf_example.py)
|      | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | N/A | [pmf_ratio.py](examples/pmf_ratio.py)
|      | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
|      | [Social Recommendation using PMF (SoRec)](cornac/models/sorec), [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) | N/A | [sorec_filmtrust.py](examples/sorec_filmtrust.py)
|      | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | N/A | [knn_movielens.py](examples/knn_movielens.py)
|      | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | [requirements.txt](cornac/models/wmf/requirements.txt) | [wmf_exp.py](examples/wmf_example.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](https://github.com/PreferredAI/cornac/issues).

## 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](http://jmlr.org/papers/v21/19-805.html), 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](https://ieeexplore.ieee.org/abstract/document/9354572), 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](https://dl.acm.org/doi/10.1145/3460231.3473324), 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](LICENSE)




%package help
Summary:	Development documents and examples for cornac
Provides:	python3-cornac-doc
%description help
# 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](https://cornac.preferred.ai/) |
[Documentation](https://cornac.readthedocs.io/en/latest/index.html) |
[Tutorials](tutorials#tutorials) |
[Examples](https://github.com/PreferredAI/cornac/tree/master/examples#cornac-examples-directory) |
[Models](#models) |
[Datasets](./cornac/datasets/README.md#datasets) |
[Paper](http://www.jmlr.org/papers/volume21/19-805/19-805.pdf) |
[Preferred.AI](https://preferred.ai/)

[![.github/workflows/python-package.yml](https://github.com/PreferredAI/cornac/actions/workflows/python-package.yml/badge.svg)](https://github.com/PreferredAI/cornac/actions/workflows/python-package.yml)
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[![Codecov](https://img.shields.io/codecov/c/github/PreferredAI/cornac/master.svg?logo=codecov)](https://codecov.io/gh/PreferredAI/cornac)
[![Docs](https://img.shields.io/readthedocs/cornac/latest.svg)](https://cornac.readthedocs.io/en/latest)
<br />
[![Release](https://img.shields.io/github/release-pre/PreferredAI/cornac.svg)](https://github.com/PreferredAI/cornac/releases)
[![PyPI](https://img.shields.io/pypi/v/cornac.svg)](https://pypi.org/project/cornac/)
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[![Conda Recipe](https://img.shields.io/badge/recipe-cornac-green.svg)](https://github.com/conda-forge/cornac-feedstock)
[![Downloads](https://static.pepy.tech/personalized-badge/cornac?period=total&units=international_system&left_color=grey&right_color=orange&left_text=downloads)](https://pepy.tech/project/cornac)
<br />
[![Python](https://img.shields.io/pypi/pyversions/cornac.svg)](https://cornac.preferred.ai/)
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[![License](https://img.shields.io/badge/License-Apache%202.0-yellow.svg)](https://opensource.org/licenses/Apache-2.0)


## Installation

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

- **From PyPI (you may need a C++ compiler):**
  ```bash
  pip3 install cornac
  ```

- **From Anaconda:**
  ```bash
  conda install cornac -c conda-forge
  ```

- **From the GitHub source (for latest updates):**
  ```bash
  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](README.md#Models).

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:
```bash
brew install gcc | brew link gcc
```

## Getting started: your first Cornac experiment

![](flow.jpg)
<p align="center"><i>Flow of an Experiment in Cornac</i></p>

```python
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](cornac/models/mf)   | 0.7430 | 0.8998 | 0.7445 |  0.0407 |  0.0479 |       0.0437 |    0.0352 |       0.13 |     1.57 |
| [PMF](cornac/models/pmf) | 0.7534 | 0.9138 | 0.7744 |  0.0491 |  0.0617 |       0.0533 |    0.0479 |       2.18 |     1.64 |
| [BPR](cornac/models/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](examples) as well as [tutorials](tutorials). For learning purposes, this list of [tutorials on recommender systems](https://github.com/PreferredAI/tutorials/tree/master/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)](cornac/models/bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | [requirements.txt](cornac/models/bivaecf/requirements.txt) | [PreferredAI/bi-vae](https://github.com/PreferredAI/bi-vae)
|      | [Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | [requirements.txt](cornac/models/causalrec/requirements.txt) | [causalrec_clothing.py](examples/causalrec_clothing.py)
|      | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | N/A | [PreferredAI/ComparER](https://github.com/PreferredAI/ComparER)
| 2020 | [Adversarial Training Towards Robust Multimedia Recommender System (AMR)](cornac/models/amr), [paper](https://ieeexplore.ieee.org/document/8618394) | [requirements.txt](cornac/models/amr/requirements.txt) | [amr_clothing.py](examples/amr_clothing.py)
| 2019 | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | N/A | [ease_movielens.py](examples/ease_movielens.py)
| 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | N/A | [c2pf_exp.py](examples/c2pf_example.py)
|      | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | N/A | [mter_exp.py](examples/mter_example.py)
|      | [Neural Attention Rating Regression with Review-level Explanations (NARRE)](cornac/models/narre), [paper](http://www.thuir.cn/group/~YQLiu/publications/WWW2018_CC.pdf) | [requirements.txt](cornac/models/narre/requirements.txt) | [narre_example.py](examples/narre_example.py)
|      | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | [requirements.txt](cornac/models/pcrl/requirements.txt) | [pcrl_exp.py](examples/pcrl_example.py)
|      | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | [requirements.txt](cornac/models/vaecf/requirements.txt) | [vaecf_citeulike.py](examples/vaecf_citeulike.py)
| 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | [requirements.txt](cornac/models/cvae/requirements.txt) | [cvae_exp.py](examples/cvae_example.py)
|      | [Conditional Variational Autoencoder for Collaborative Filtering (CVAECF)](cornac/models/cvaecf), [paper](https://seslab.kaist.ac.kr/xe2/?module=file&act=procFileDownload&file_srl=18019&sid=4be19b9d0134a4aeacb9ef1ecd81c784&module_srl=1379) | [requirements.txt](cornac/models/cvaecf/requirements.txt) | [cvaecf_filmtrust.py](examples/cvaecf_filmtrust.py)
|      | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/ibpr/requirements.txt) | [ibpr_exp.py](examples/ibpr_example.py)
|      | [Matrix Co-Factorization (MCF)](cornac/models/mcf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | N/A | [mcf_office.py](examples/mcf_office.py)
|      | [Multi-Layer Perceptron (MLP)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Neural Matrix Factorization (NeuMF) / Neural Collaborative Filtering (NCF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
|      | [Online Indexable Bayesian Personalized Ranking (Online IBPR)](cornac/models/online_ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/online_ibpr/requirements.txt) |
|      | [Visual Matrix Factorization (VMF)](cornac/models/vmf), [paper](http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p1113.pdf) | [requirements.txt](cornac/models/vmf/requirements.txt) | [vmf_clothing.py](examples/vmf_clothing.py)
| 2016 | [Collaborative Deep Ranking (CDR)](cornac/models/cdr), [paper](http://inpluslab.com/chenliang/homepagefiles/paper/hao-pakdd2016.pdf) | [requirements.txt](cornac/models/cdr/requirements.txt) | [cdr_exp.py](examples/cdr_example.py)
|      | [Collaborative Ordinal Embedding (COE)](cornac/models/coe), [paper](http://www.hadylauw.com/publications/sdm16.pdf) | [requirements.txt](cornac/models/coe/requirements.txt) |
|      | [Convolutional Matrix Factorization (ConvMF)](cornac/models/conv_mf), [paper](http://uclab.khu.ac.kr/resources/publication/C_351.pdf) | [requirements.txt](cornac/models/conv_mf/requirements.txt) | [convmf_exp.py](examples/conv_mf_example.py)
|      | [Spherical K-means (SKM)](cornac/models/skm), [paper](https://www.sciencedirect.com/science/article/pii/S092523121501509X) | N/A | [skm_movielens.py](examples/skm_movielens.py)
|      | [Visual Bayesian Personalized Ranking (VBPR)](cornac/models/vbpr), [paper](https://arxiv.org/pdf/1510.01784.pdf) | [requirements.txt](cornac/models/vbpr/requirements.txt) | [vbpr_tradesy.py](examples/vbpr_tradesy.py)
| 2015 | [Collaborative Deep Learning (CDL)](cornac/models/cdl), [paper](https://arxiv.org/pdf/1409.2944.pdf) | [requirements.txt](cornac/models/cdl/requirements.txt) | [cdl_exp.py](examples/cdl_example.py)
|      | [Hierarchical Poisson Factorization (HPF)](cornac/models/hpf), [paper](http://jakehofman.com/inprint/poisson_recs.pdf) | N/A | [hpf_movielens.py](examples/hpf_movielens.py)
| 2014 | [Explicit Factor Model (EFM)](cornac/models/efm), [paper](http://yongfeng.me/attach/efm-zhang.pdf) | N/A | [efm_exp.py](examples/efm_example.py)
|      | [Social Bayesian Personalized Ranking (SBPR)](cornac/models/sbpr), [paper](https://cseweb.ucsd.edu/~jmcauley/pdfs/cikm14.pdf) | N/A | [sbpr_epinions.py](examples/sbpr_epinions.py)
| 2013 | [Hidden Factors and Hidden Topics (HFT)](cornac/models/hft), [paper](https://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf) | N/A | [hft_exp.py](examples/hft_example.py)
| 2012 | [Weighted Bayesian Personalized Ranking (WBPR)](cornac/models/bpr), [paper](http://proceedings.mlr.press/v18/gantner12a/gantner12a.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
| 2011 | [Collaborative Topic Regression (CTR)](cornac/models/ctr), [paper](http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf) | N/A | [ctr_citeulike.py](examples/ctr_example_citeulike.py)
| Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
|      | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
|      | [Factorization Machines (FM)](cornac/models/fm), [paper](https://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf) | Linux only | [fm_example.py](examples/fm_example.py)
|      | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py)
|      | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | N/A | [knn_movielens.py](examples/knn_movielens.py)
|      | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py), [given_data.py](examples/given_data.py)
|      | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | N/A | [mmmf_exp.py](examples/mmmf_exp.py)
|      | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
|      | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | N/A | [nmf_exp.py](examples/nmf_example.py)
|      | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | N/A | [pmf_ratio.py](examples/pmf_ratio.py)
|      | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://people.engr.tamu.edu/huangrh/Spring16/papers_course/matrix_factorization.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
|      | [Social Recommendation using PMF (SoRec)](cornac/models/sorec), [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) | N/A | [sorec_filmtrust.py](examples/sorec_filmtrust.py)
|      | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | N/A | [knn_movielens.py](examples/knn_movielens.py)
|      | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | [requirements.txt](cornac/models/wmf/requirements.txt) | [wmf_exp.py](examples/wmf_example.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](https://github.com/PreferredAI/cornac/issues).

## 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](http://jmlr.org/papers/v21/19-805.html), 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](https://ieeexplore.ieee.org/abstract/document/9354572), 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](https://dl.acm.org/doi/10.1145/3460231.3473324), 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](LICENSE)




%prep
%autosetup -n cornac-1.15.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-cornac -f filelist.lst
%dir %{python3_sitearch}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.15.0-1
- Package Spec generated