Workshop on News Recom mendation and Analytics (INRA'23) Benjamin Kille benjamin.u.kille@ntnu.no 0 3 Andreas Lommatzsch andreas.lommatzsch@dai-labor.de 2 3 Özlem Özgöbek 0 3 Peng Liu peng.liu@ntnu.no 0 3 Lemei Zhang lemei.zhang@ntnu.no 0 3 Simen Eide simen.eide@schibsted.com 1 3 Norwegian University of Science and Technology , Sem Saerlands Vei 5, 7034 Trondheim , Norway Schibsted ASA , Akersgata 55, 0180 Oslo , Norway Technische Universität Berlin , Ernst-Reuter-Platz 7, 10587 Berlin , Germany Michael Beam, Kent State University • Simen Eide , Schibsted ASA • Mehdi Elahi , University Bergen • Dietmar Jannach, Adria-Alpen Universität Klagenfurt • Benjamin Kille, Norwegian University of Science and Technology • Peng Liu, Norwegian University of Science and Technology • Andreas Lommatzsch, Technische Universität Berlin • Christoph Trattner, University Bergen • Lemei Zhang, Norwegian University of Science and Technology • Özlem Özgöbek, Norwegian University of Science and Technology 2023

News convey important information to citizens. The past years have seen an increase in the use of personalization techniques on news platforms. At the same time, Generative Artificial Intelligence (AI) has emerged as a tool to further automate the publishing process. The International Workshop on News Recommendation and Analytics provides a venue for presentations, discussions, and the exchange of ideas concerning recent trends in news. The Eleventh edition is co-located with the ACM Conference on Recommender Systems 2023. A diverse crowed of researchers, practitioners, and interested third parties attended the event.

 Democracies need a functioning news eco-system to assure the required flow of information to -

∗Corresponding author.

These questions and more motivate us to organize the International Workshop on News Recommendation and Analytics. The workshop series has become an annual tradition, providing researchers, practitioners, and interested third parties a forum to come together and exchange ideas, discuss recent developments, and contemplate eforts to better understand the efects of AI on news. This year marks the eleventh edition that was co-located with the ACM Conference on Recommender Systems in Singapore.

 2. Accepted Contributions

The call for participation motivated researchers to submit their findings in the form of regular or short papers. Nine groups of authors submitted their work. A thorough peer-review found six of the nine manuscript suited for presentation:

 3. Program Committee

We recruited a set of expert to assure the quality of the accepted papers. We would like to thank:

 1. “ It's just a robot that looks at numbers”: Restoring Journalistic Voice in News Recommendation, by Nihal Alaqabawy, Aishwarya Satwani , Amy Voida, and Robin Burke. 2. A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, and Fake News, by Xinyi Li, Yongfeng Zhang , and Edward C. Malthouse . 3. NeMig-A Bilingual News Collection and Knowledge Graph about Migration, by Andreea Iana, Mehwish Alam , Alexander Grote, Nevena Nikolajevic, Katharina Ludwig, Philipp Müller, Christof Weinhardt, Heiko Paulheim. 4. Topical Preference Trumps Other Features in News Recommendation: A Conjoint Analysis on a Representative Sample from Norway, by Erik Knudsen , Alain D. Starke , and Christoph Trattner. 5. On the Efect of Incorporating Expressed Emotions in News Articles on Diversity within Recommendation Models, by Mete Sertkan and Julia Neidhardt . 6. Like a Skilled DJ-an expert Study on News Recommendation Beyond Accuracy , by Thomas E. Kolb, Irina Nalis, and Julia Neidhardt.