Informfully Documentation

Welcome to Informfully (GitHub & Website)! Informfully is an open-source reproducibility platform for content distribution and user experiments.

To view the full documentation, please visit Informfully at Read the Docs. It is the combined documentation for all code repositories.

Links and Resources: GitHub | Website | X | Documentation | DDIS@UZH | Google Play | App Store

Overview

The documentation in this repository is the source for the online Informfully Documentation. It can be downloaded for offline use. Otherwise, please use the online version instead.

# Download the documentation
git clone https://github.com/Informfully/Documentation.git

Presentations and Posters

Title	Venue
Diverse News Recommendations	FOJ '21
Algorithmic Content Curation	ICA '22
Deliberateive Diversity for News Recommendations	RecSys '23
Classification of Recommender Systems	NORMalize '23
Policital News Recommendations	ICA '23
Informfully Platform	RecSys '24
Diversity in News Recommendations	RecSys '24
Nudging News Engagement	ICA '24
Informfully Dataset	NORMalize '24
Beyond-Accuracy Objectives for AI in News	AMLD '25
Informfully Recommenders	RecSys '25
Diversity-Driven Random Walks (D-RDW)	RecSys '25
Nudges for News Recommenders	NORMalize '25

Citation

If you use any code or data from this repository in a scientific publication, we ask you to cite the following papers:

• Informfully - Research Platform for Reproducible User Studies, Heitz et al., Proceedings of the 18th ACM Conference on Recommender Systems, 2024.

  @inproceedings{heitz2024informfully,
    title={Informfully - Research Platform for Reproducible User Studies},
    author={Heitz, Lucien and Croci, Julian A and Sachdeva, Madhav and Bernstein, Abraham},
    booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
    pages={660--669},
    year={2024}
  }

• Deliberative Diversity for News Recommendations - Operationalization and Experimental User Study, Heitz et al., Proceedings of the 17th ACM Conference on Recommender Systems, 813–819, 2023.

  @inproceedings{heitz2023deliberative,
    title={Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study},
    author={Heitz, Lucien and Lischka, Juliane A and Abdullah, Rana and Laugwitz, Laura and Meyer, Hendrik and Bernstein, Abraham},
    booktitle={Proceedings of the 17th ACM Conference on Recommender Systems},
    pages={813--819},
    year={2023}
  }

Contributing

You are welcome to contribute to the Informfully ecosystem and become a part of our community. Feel free to:

• Fork any of the Informfully repositories.
• Suggest new features in Future Release.
• Make changes and create pull requests.

Please post your feature requests and bug reports in our GitHub issues section.

License

Released under the MIT License. (Please note that the respective copyright licenses of third-party libraries and dependencies apply.)