#26: Diversity in Recommender Systems with Sanne Vrijenhoek

In episode 26 of Recsperts, I speak with Sanne Vrijenhoek, a PhD candidate at the University of Amsterdam’s Institute for Information Law and the AI, Media & Democracy Lab. Sanne’s research explores diversity in recommender systems, particularly in the news domain, and its connection to democratic values and goals.We dive into four of her papers, which focus on how diversity is conceptualized in news recommender systems. Sanne introduces us to five rank-aware divergence metrics for measuring normative diversity and explains why diversity evaluation shouldn’t be approached blindly—first, we need to clarify the underlying values. She also presents a normative framework for these metrics, linking them to different democratic theory perspectives. Beyond evaluation, we discuss how to optimize diversity in recommender systems and reflect on missed opportunities—such as the RecSys Challenge 2024, which could have gone beyond accuracy-chasing. Sanne also shares her recommendations for improving the challenge by incorporating objectives such as diversity.During our conversation, Sanne shares insights on effectively communicating recommender systems research to non-technical audiences. To wrap up, we explore ideas for fostering a more diverse RecSys research community, integrating perspectives from multiple disciplines.Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.Don't forget to follow the podcast and please leave a review(00:00) - Introduction (03:24) - About Sanne Vrijenhoek (14:49) - What Does Diversity in RecSys Mean? (26:32) - Assessing Diversity in News Recommendations (34:54) - Rank-Aware Divergence Metrics to Measure Normative Diversity (01:01:37) - RecSys Challenge 2024 - Recommendations for the Recommenders (01:11:23) - RecSys Workshops - NORMalize and AltRecSys (01:15:39) - On the Different Conceptualizations of Diversity in RecSys (01:28:38) - Closing Remarks Links from the Episode:Sanne Vrijenhoek on LinkedInInformfullyMIND: MIcrosoft News DatasetRecSys Challenge 2024NORMalize 2023: The First Workshop on the Normative Design and Evaluation of Recommender SystemsNORMalize 2024: The Second Workshop on the Normative Design and Evaluation of Recommender SystemsAltRecSys 2024: The AltRecSys Workshop on Alternative, Unexpected, and Critical Ideas in RecommendationPapers:Vrijenhoek et al. (2021): Recommenders with a Mission: Assessing Diversity in News RecommendationsVrijenhoek et al. (2022): RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News RecommendationsHeitz et al. (2024): Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys ChallengeVrijenhoek et al. (2024): Diversity of What? On the Different Conceptualizations of Diversity in Recommender SystemsHelberger (2019): On the Democratic Role of News RecommendersSteck (2018): Calibrated RecommendationsGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

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Recommender Systems are the most challenging, powerful and ubiquitous area of machine learning and artificial intelligence. This podcast hosts the experts in recommender systems research and application. From understanding what users really want to driving large-scale content discovery - from delivering personalized online experiences to catering to multi-stakeholder goals. Guests from industry and academia share how they tackle these and many more challenges. With Recsperts coming from universities all around the globe or from various industries like streaming, ecommerce, news, or social media, this podcast provides depth and insights. We go far beyond your 101 on RecSys and the shallowness of another matrix factorization based rating prediction blogpost! The motto is: be relevant or become irrelevant! Expect a brand-new interview each month and follow Recsperts on your favorite podcast player.