Recommender Systems in Continuing Professional Education for Public Transport: Challenges of a Human-Centered Design

Author:

Libreros Jose Alejandro1ORCID,Mayas Cindy1ORCID,Hirth Matthias1ORCID

Affiliation:

1. TU Ilmenau, Germany

Funder

Bundesministerium für Bildung und Forschung

Publisher

ACM

Reference34 articles.

1. Behnoush Abdollahi and Olfa Nasraoui . 2018. Transparency in Fair Machine Learning: The Case of Explainable Recommender Systems . In Human and Machine Learning: Visible , Explainable, Trustworthy and Transparent, Jianlong Zhou and Fang Chen (Eds.). Springer International Publishing , Cham , 21–35. https://doi.org/10.1007/978-3-319-90403-0_2 10.1007/978-3-319-90403-0_2 Behnoush Abdollahi and Olfa Nasraoui. 2018. Transparency in Fair Machine Learning: The Case of Explainable Recommender Systems. In Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, Jianlong Zhou and Fang Chen (Eds.). Springer International Publishing, Cham, 21–35. https://doi.org/10.1007/978-3-319-90403-0_2

2. JaSenpai: Towards an Adaptive and Social Interactive E-Learning Platform for Japanese Language Learning

3. Comparing Peer Recommendation Strategies in a MOOC

4. Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

5. Robin Burke . 2004. Hybrid Recommender Systems with Case-Based Components . In Advances in Case-Based Reasoning, Peter Funk and Pedro A . González Calero (Eds.). Springer , 91–105. https://doi.org/10.1007/978-3-540-28631-8_8 10.1007/978-3-540-28631-8_8 Robin Burke. 2004. Hybrid Recommender Systems with Case-Based Components. In Advances in Case-Based Reasoning, Peter Funk and Pedro A. González Calero (Eds.). Springer, 91–105. https://doi.org/10.1007/978-3-540-28631-8_8

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