Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit

Author:

Vente Tobias1ORCID,Ekstrand Michael2ORCID,Beel Joeran1ORCID

Affiliation:

1. Intelligent Systems Group, University of Siegen, Germany

2. Boise State University, USA

Publisher

ACM

Reference74 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/ Software available from tensorflow.org.

2. Rohan Anand and Joeran Beel . 2020 . Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization. In RecSys 2020: Fourteenth ACM Conference on Recommender Systems , Virtual Event , Brazil, September 22-26, 2020, Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, and Edleno Silva de Moura (Eds.). ACM, 585–587. https://doi.org/10.1145/3383313.3411467 10.1145/3383313.3411467 Rohan Anand and Joeran Beel. 2020. Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization. In RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22-26, 2020, Rodrygo L. T. Santos, Leandro Balby Marinho, Elizabeth M. Daly, Li Chen, Kim Falk, Noam Koenigstein, and Edleno Silva de Moura (Eds.). ACM, 585–587. https://doi.org/10.1145/3383313.3411467

3. Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

4. Fairness metrics and bias mitigation strategies for rating predictions

5. Michał Bałchanowski and Urszula Boryczka . 2022. Collaborative Rank Aggregation in Recommendation Systems. Procedia computer science 207 (Jan . 2022 ), 2213–2222. https://doi.org/10.1016/j.procs.2022.09.281 10.1016/j.procs.2022.09.281 Michał Bałchanowski and Urszula Boryczka. 2022. Collaborative Rank Aggregation in Recommendation Systems. Procedia computer science 207 (Jan. 2022), 2213–2222. https://doi.org/10.1016/j.procs.2022.09.281

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