rectorch v0.0.9-beta.0

In this second rectorch release we added two new methods.

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

[1] 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

[2] 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

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

[1] 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

[2] 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

[3] 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

[4] 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