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Hackathon-Challenge-Pitch_221124.pdf

Sina
November 25, 2022
130

 Hackathon-Challenge-Pitch_221124.pdf

Sina

November 25, 2022
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Transcript

  1. Who? Research Project on «The Role of News Recommender Systems

    in Digital Democracies» Sina Blassnig1, Edina Strikovic2, Eliza Mitova1, Aleksandra Urman3, Anikó Hannák3, Frank Esser1, & Claes de Vreese2, 1 Department of Communication and Media Research, University of Zürich 2 Amsterdam School of Communication Research, University of Amsterdam 3 Department of Informatics, University of Zürich Contact: [email protected] 25.11.2022 Page 1
  2. Why? 25.11.2022 Page 2 – Changing user behavior – Increasing

    automation and personalization of news Source: Digital News Report 2022, Data for Switzerland
  3. What are News Recommender Systems (NRS)? – Algorithms that can

    make automated and/or personalized recommendations based on metadata, past behavior, ratings of similar users, popularity, and/or content-specific features (Ricci et al., 2011) – NRS can be used on the frontpage of news media websites, below articles, in special sections, for newsletters, etc. 25.11.2022 Page 3
  4. What is the problem? – Good NRS are expensive, laborious

    and complicated. – That’s why media still often focus only on: – Topic similarity – Popularity 25.11.2022 Page 4
  5. What is the Challenge? 1) Analyze the problem: – What

    gets left behind if we focus on popularity-based recommenders? what categories, e.g., topics, regions, actors, formats etc. are over- or underrepresented 2) Provide a solution: – What would need to be pushed in a diversity-maximizing recommender? what categories, e.g., topics, regions, actors, formats etc. – Can this be done in a transparent way? E.g., by showing why something is recommended 25.11.2022 Page 5
  6. Potential Resources & Needed Data Needed: − Content archive (articles,

    videos, audio) − Aggregated user data (e.g., likes, clicks, views, time spent) Available: - SRG APIs: https://developer.srgssr.ch/apis/ - Others? 25.11.2022 Page 6