Research Platform for Reproducible User Studies

Informfully is a research platform for content distribution and user studies. The platform allows to push algorithmically curated text, image, audio, and video content to users and automatically generates a detailed log of their consumption history. It is an open-source platform for conducting user experiments to investigate the impact of item recommendations on users’ consumption behavior. You can get more information here.

RecSys '24: Resource Paper

Click here to download our RecSys '24 paper submission on the Informfully platform:

Download RecSys Resource Paper

RecSys'24 Presentation

RecSys '24: Challenge Paper

Click here to download our RecSys '24 challenge paper submission on diversity in news recommendations:

Download RecSys Challenge Paper

RecSys'24 Challenge Presentation

RecSys '24: NORMalize Workshop Paper

Download the NORMalize paper on our Informfully dataset IDEA:

Download NORMalize Workshop Paper

NORMalize'24 Presentation

We are hiring!

Are you interested in large-scale recommender systems and saving society? Then apply to our open position:

PhD Position at the DDIS@UZH

Related: Deliberative Diversity in News Recommender Systems

Click here to download our RecSys '23 paper submission on 'Deliberative Diversity for News Recommenders':

Download Research Paper

Related: Benefits of Diverse News Recommendations for Democracy

Read our previous journal article on the impact of diviersity-optimized recommenders for participatory diversity:

Download Research Paper

Related: Research Manifesto

Informfully is part of a research endeavor of investigating the normative aspects of news recommendations. Click here to download community manifesto:

Download Research Manifesto

Related: OSCE Policy Manual

We take part in the discussion of the impact of algorithmic content curation and AI at the OSCE level. Click here to get the most recent policy manual:

Download OSCE Book