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Recsperts - Recommender Systems ExpertsRecommender Systems are the most challenging, powerful and ubiquitous area of machine learning and artificial intelligence. Author: Marcel Kurovski
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. Language: en Genres: Mathematics, Science, Technology Contact email: Get it Feed URL: Get it iTunes ID: Get it |
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#28: Multistakeholder Recommender Systems with Robin Burke
Episode 29
Monday, 14 April, 2025
In episode 28 of Recsperts, I sit down with Robin Burke, professor of information science at the University of Colorado Boulder and a leading expert with over 30 years of experience in recommender systems. Together, we explore multistakeholder recommender systems, fairness, transparency, and the role of recommender systems in the age of evolving generative AI.We begin by tracing the origins of recommender systems, traditionally built around user-centric models. However, Robin challenges this perspective, arguing that all recommender systems are inherently multistakeholder—serving not just consumers as the recipients of recommendations, but also content providers, platform operators, and other key players with partially competing interests. He explains why the common “Recommended for You” label is, at best, an oversimplification and how greater transparency is needed to show how stakeholder interests are balanced.Our conversation also delves into practical approaches for handling multiple objectives, including reranking strategies versus integrated optimization. While embedding multistakeholder concerns directly into models may be ideal, reranking offers a more flexible and efficient alternative, reducing the need for frequent retraining.Towards the end of our discussion, we explore post-userism and the impact of generative AI on recommendation systems. With AI-generated content on the rise, Robin raises a critical concern: if recommendation systems remain overly user-centric, generative content could marginalize human creators, diminishing their revenue streams. 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 Robin Burke and First Recommender Systems (26:07) - From Fairness and Advertising to Multistakeholder RecSys (34:10) - Multistakeholder RecSys Terminology (40:16) - Multistakeholder vs. Multiobjective (42:43) - Reciprocal and Value-Aware RecSys (59:14) - Objective Integration vs. Reranking (01:06:31) - Social Choice for Recommendations under Fairness (01:17:40) - Post-Userist Recommender Systems (01:26:34) - Further Challenges and Closing Remarks Links from the Episode:Robin Burke on LinkedInRobin's WebsiteThat Recommender Systems LabReference to Broder's Keynote on Computational Advertising and Recommender Systems from RecSys 2008Multistakeholder Recommender Systems (from Recommender Systems Handbook), chapter by Himan Abdollahpouri & Robin BurkePOPROX: The Platform for OPen Recommendation and Online eXperimentationAltRecSys 2024 (Workshop at RecSys 2024)Papers:Burke et al. (1996): Knowledge-Based Navigation of Complex Information SpacesBurke (2002): Hybrid Recommender Systems: Survey and ExperimentsResnick et al. (1997): Recommender SystemsGoldberg et al. (1992): Using collaborative filtering to weave an information tapestryLinden et al. (2003): Amazon.com Recommendations - Item-to-Item Collaborative FilteringAird et al. (2024): Social Choice for Heterogeneous Fairness in RecommendationAird et al. (2024): Dynamic Fairness-aware Recommendation Through Multi-agent Social ChoiceBurke et al. (2024): Post-Userist Recommender Systems : A ManifestoBaumer et al. (2017): Post-userismBurke et al. (2024): Conducting Recommender Systems User Studies Using POPROXGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website
