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Follow my work on news, politics, and recommender systems:
- Informfully Recommenders – Reproducibility Framework for Diversity-aware Intra-session Recommendations
- D-RDW: Diversity-Driven Random Walks for News Recommender Systems
- NewsImages in MediaEval 2025 – Comparing Image Retrieval and Generation for News Articles
- Nudges for News Recommenders
- Adaptive Political Surveys and GPT-4: Tackling the Cold Start Problem with Simulated User Interactions
- IDEA – Informfully Dataset with Enhanced Attributes
- Informfully – Research Platform for Reproducible User Studies
- Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys Challenge
- An Empirical Exploration of Perceived Similarity Between News Article Texts and Images
- Prompt-based Alignment of Headlines and Images Using OpenCLIP
- Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study
- Classification of Normative Recommender Systems
- Benefits of Diverse News Recommendations for Democracy: A User Study
- Spotlight on Artificial Intelligence and Freedom of Expression
- AI in Content Curation and Media Pluralism
- Exploring Graph-querying Approaches in LifeGraph
- VideoGraph – Towards Using Knowledge Graphs for Interactive Video Retrieval
- Diversity in News Recommendation
If you have any questions about Informfully or my research, feel free to connect on LinkedIn or reach out to me via e-mail: info@informfully.ch