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https://github.com/ylongqi/openrec

OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
https://github.com/ylongqi/openrec

deep-learning deep-neural-networks machine-learning ml recommendation recommendation-algorithm recommendation-system

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OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms

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[**OpenRec**](http://www.openrec.ai/) is an open-source and modular library for neural network-inspired recommendation algorithms. Each recommender is modeled as a computational graph that consists of a structured ensemble of reusable modules connected through a set of well-defined interfaces. OpenRec is built to ease the process of extending and adapting state-of-the-art neural recommenders to heterogeneous recommendation scenarios, where different users', items', and contextual data sources need to be incorporated.



**For the structure and the design philosophy of OpenRec, please refer to the following paper published in WSDM'18:** 



[Longqi Yang](https://ylongqi.com/), Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, and [Deborah Estrin](http://destrin.smalldata.io/). 2018. [OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms.](https://ylongqi.com/paper/YangBGHE18.pdf) In Proceedings of WSDM’18, February 5–9, 2018, Marina Del Rey, CA, USA.  



**2020-02-17** OpenRec now uses Tensorflow 2.0 by default. Supports for Tensorflow 1.x are deprecated (all prior APIs have been moved to `openrec.tf1`). Currently supported recommendation algorithms include:

* BPR (`openrec.tf2.recommenders.BPR`): Bayesian Personalized Ranking (Rendle et al., 2009)

* WRMF (`openrec.tf2.recommenders.WRMF`): Weighted Regularized Matrix Factorization (Hu et al., 2008)

* UCML (`openrec.tf2.recommenders.UCML`): Collaborative Metric Learning with uniformly sampled triplets (Hsieh et al., 2017)

* GMF (`openrec.tf2.recommenders.GMF`): Generalized Matrix Factorization, a.k.a., Neural Collaborative Filtering (He et al., 2017)

* DLRM (`openrec.tf2.recommenders.DLRM`): Deep Learning Recommendation Model, developed by Facebook (Naumov et al., 2019)



**2019-07-12** OpenRec is being migrated to [Tensorflow 2.0](https://www.tensorflow.org/beta). Major changes to expect:



- All OpenRec modules will be compatible with [tf.keras.layers.Layer](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/Layer), so that they can be used seamlessly with any Tensorflow 2.0 code base.

- All OpenRec models will be compatible with [tf.keras.Model](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model).

- All input data pipelines will be compatible with [tf.data.Dataset](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/data/Dataset) but are made much more friendly for recommendation models.

- Minimizing boilerplate while keeping the modularity and adaptability of OpenRec.



To get things started, we introduce OpenRec (Tensorflow 2.0) implementations of [deep learning recommendation model (DLRM)](https://github.com/facebookresearch/dlrm). Check out `tf2_examples/dlrm_criteo.py`.



To experiment with these new features, do `pip3 install .` inside the repo and then `import openrec.tf2`. You need to have Tensorflow 2.0 installed (Follow the instructions [here](https://www.tensorflow.org/beta)).



More examples, tutorials and documents will be available soon. Check out `tf2_examples/`.



**2018-08-31** Introducing new modular interfaces for OpenRec. Major changes:



- A new paradigm for defining, extending, and building recommenders.

  - Remove boilerplate class structure of recommenders.

  - Introduce a macro-based recommender construction paradigm.

  - Disentangle module construction and connection.

  - Support module construction directly using Tensorflow and Keras APIs.

- A more efficient and customizable pipeline for recommender training and evaluation.

  - A new Dataset class for complex data input.

  - A customizable ModelTrainer handling complex training/evaluation scenarios.

  - Caching mechanism to speed up evaluation of complex recommenders.

- Provide model training and evaluation examples for new interfaces.



More recommenders, examples, documents and tutorials are under development. Please checkout following events where we will present OpenRec new features:



*Strata Data Conference 2018:* https://conferences.oreilly.com/strata/strata-ny/public/schedule/detail/68280



*Recsys 2018:* https://recsys.acm.org/recsys18/tutorials/#content-tab-1-1-tab



**To use original openrec, simply import `openrec.legacy`**.



## Installation



Before installing OpenRec, please install [TensorFlow backend](https://www.tensorflow.org/install/) (GPU version is recommended). 



- **Install OpenRec from PyPI (recommended):**



```sh

pip install openrec

```



- **Install OpenRec from source code:**



First, clone OpenRec using `git`:



```sh

git clone https://github.com/ylongqi/openrec

```



Then, `cd` to the OpenRec folder and run the install command:



```sh

cd openrec

python setup.py install

```



## Dataset download



All datasets can be downloaded from Google drive [here](https://drive.google.com/drive/folders/1taJ91txiMAWBMUtezc_N5gaYuTEpvW_e?usp=sharing).



## Get started



* [OpenRec website](http://www.openrec.ai/)

* [OpenRec legacy documents](http://openrec.readthedocs.io/en/latest/)

* [OpenRec legacy tutorials](https://github.com/ylongqi/openrec/tree/master/legacy_tutorials)

* [OpenRec legacy examples](https://github.com/ylongqi/openrec/tree/master/legacy_examples)



## How to cite



```

@inproceedings{yang2018openrec,

  title={OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms},

  author={Yang, Longqi and Bagdasaryan, Eugene and Gruenstein, Joshua and Hsieh, Cheng-Kang and Estrin, Deborah},

  booktitle={Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining},

  year={2018},

  organization={ACM}

}

```



## License



[Apache License 2.0](LICENSE)



## Funders