Markus Reiter-Haas

According to our database, Markus Reiter-Haas authored at least 19 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Can Platform Design Encourage Curiosity? Evidence from an Independent Social Media Experiment.

Marie Neubrander, Markus Reiter-Haas, Ben Rochford, Max Allamong, Christopher Bail, Sunshine Hillygus, Alexander Volfovsky

CoRR, January, 2026

Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networks.

Kevin Innerebner, Stephan Bartl, Markus Reiter-Haas, Elisabeth Lex

Proceedings of the Advances in Information Retrieval, 2026

2025

The climate gluing protests: analyzing their development and framing in media since 1986 using sentiment analyses and frame detection models.

Markus Hadler, Alexander Ertl, Beate Klösch, Markus Reiter-Haas, Elisabeth Lex

Frontiers Big Data, 2025

Mind the Gap: Urban-Rural Disparities in Wheelchair Accessibility for POI Recommendations.

Gregor Mayr, Markus Reiter-Haas, Elisabeth Lex

Proceedings of the Recommender Systems for Sustainability and Social Good, 2025

Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit.

Markus Reiter-Haas, Elisabeth Lex

Proceedings of the 13th International Workshop on News Recommendation and Analytics co-located with the 2025 ACM Conference on Recommender Systems (RecSys 2025), 2025

2024

Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media.

Markus Reiter-Haas, Beate Klösch, Markus Hadler, Elisabeth Lex

CoRR, 2024

The Framing Loop: Do Users Repeatedly Read Similar Framed News Online?

Markus Reiter-Haas, Elisabeth Lex

Proceedings of the Joint Proceedings of the ACM IUI 2024 Workshops co-located with the 29th Annual ACM Conference on Intelligent User Interfaces (IUI 2024), 2024

Computational Narrative Framing: Towards Identifying Frames through Contrasting the Evolution of Narrations.

Markus Reiter-Haas, Beate Klösch, Markus Hadler, Elisabeth Lex

Proceedings of Text2Story, 2024

FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines.

Markus Reiter-Haas, Beate Klösch, Markus Hadler, Elisabeth Lex

Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval, 2024

2023

Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation.

Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl

Dataset, May, 2023

Exploration of Framing Biases in Polarized Online Content Consumption.

Markus Reiter-Haas

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection.

Markus Reiter-Haas, Alexander Ertl, Kevin Innerhofer, Elisabeth Lex

Proceedings of the The 17th International Workshop on Semantic Evaluation, 2023

Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation.

Marta Moscati, Christian Wallmann, Markus Reiter-Haas, Dominik Kowald, Elisabeth Lex, Markus Schedl

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

2021

Predicting Music Relistening Behavior Using the ACT-R Framework.

Markus Reiter-Haas, Emilia Parada-Cabaleiro, Markus Schedl, Elham Motamedi, Marko Tkalcic, Elisabeth Lex

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Studying Moral-based Differences in the Framing of Political Tweets.

Markus Reiter-Haas, Simone Kopeinik, Elisabeth Lex

Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, 2021

2020

Using autoencoders for session-based job recommendations.

Emanuel Lacic, Markus Reiter-Haas, Dominik Kowald, Manoj Reddy Dareddy, Junghoo Cho, Elisabeth Lex

User Model. User Adapt. Interact., 2020

On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student Career.

Markus Reiter-Haas, David Wittenbrink, Emanuel Lacic

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

2019

Should we embed?: a study on the online performance of utilizing embeddings for real-time job recommendations.

Emanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, Elisabeth Lex

Proceedings of the 13th ACM Conference on Recommender Systems, 2019

2017

Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations.

Emanuel Lacic, Dominik Kowald, Markus Reiter-Haas, Valentin Slawicek, Elisabeth Lex

CoRR, 2017