Rectorch

In this second **rectorch** release we added two new methods.

| Name | Description | Ref. |
|-----------|----------------------------------------------------------------------------|-----------|
|ADMM_Slim | ADMM SLIM: Sparse Recommendations for Many Users | [[1]](1) |
| SVAE | Sequential Variational Autoencoders for Collaborative Filtering | [[2]](2) |

We also added/changed the following functionalities:
* **metrics**: `MRR`, `Hit`;
* **evaluation**: `one_plus_random`, `ValidFunc`;
* **models**: removed the `validate` method;
* **data**: added method `load_data_ad_dict` to `DataReader`.

References

<a id="1">[1]</a> Harald Steck, Maria Dimakopoulou, Nickolai Riabov, and Tony Jebara. 2020.
ADMM SLIM: Sparse Recommendations for Many Users. In Proceedings of the 13th International
Conference on Web Search and Data Mining (WSDM ’20). Association for Computing Machinery,
New York, NY, USA, 555–563. DOI: https://doi.org/10.1145/3336191.3371774

<a id="2">[2]</a> Noveen Sachdeva, Giuseppe Manco, Ettore Ritacco, and Vikram Pudi. 2019.
Sequential Variational Autoencoders for Collaborative Filtering. In Proceedings of the Twelfth
ACM International Conference on Web Search and Data Mining (WSDM ’19). Association for Computing
Machinery, New York, NY, USA, 600–608. DOI: https://doi.org/10.1145/3289600.3291007

First **rectorch** release.

The release includes the following methods.

| Name | Description | Ref. |
|-----------|----------------------------------------------------------------------------|-----------|
| MultiDAE | Denoising Autoencoder for Collaborative filtering with Multinomial prior | [[1]](1) |
| MultiVAE | Variational Autoencoder for Collaborative filtering with Multinomial prior | [[1]](1) |
| CMultiVAE | Conditioned Variational Autoencoder | [[2]](2) |
| CFGAN | Collaborative Filtering with Generative Adversarial Networks | [[3]](3) |
| EASE | Embarrassingly shallow autoencoder for sparse data | [[4]](4) |

References
<a id="1">[1]</a> Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018.
Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018
World Wide Web Conference (WWW ’18). International World Wide Web Conferences Steering
Committee, Republic and Canton of Geneva, CHE, 689–698.
DOI: https://doi.org/10.1145/3178876.3186150

<a id="2">[2]</a> Tommaso Carraro, Mirko Polato and Fabio Aiolli. Conditioned Variational
Autoencoder for top-N item recommendation, 2020. arXiv pre-print:
https://arxiv.org/abs/2004.11141

<a id="3">[3]</a> Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018.
CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks.
In Proceedings of the 27th ACM International Conference on Information and Knowledge
Management (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 137–146.
DOI: https://doi.org/10.1145/3269206.3271743

<a id="4">[4]</a> Harald Steck. 2019. Embarrassingly Shallow Autoencoders for Sparse Data.
In The World Wide Web Conference (WWW ’19). Association for Computing Machinery,
New York, NY, USA, 3251–3257. DOI: https://doi.org/10.1145/3308558.3313710