makgyver/rectorch: rectorch v0.0.6-beta.0

First rectorch release.

The release includes the following methods.

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