Sep 2026· Proceedings of the 20th ACM Conference on Recommender Systems· 0 citations· 22 references
TL;DR
This work reimagines recommender systems not merely as engines of engagement, but as accountable infrastructures that uphold democratic values that affect both individuals and society at large.
Abstract
Recommender systems shape how people shop, learn, and read the news, and advances in algorithms have made personalization increasingly influential. Yet most systems are optimized for accuracy and engagement without asking the normative question of what recommender systems should be. When we fail to ask this question, we risk systems that amplify misinformation, reinforce biases, and create echo chambers that affect both individuals and society at large. News recommendation is a powerful case in point: while it plays a pivotal role in shaping public opinion, current systems lack mechanisms to include journalists’ voices, values, and perspectives. My work addresses this gap through (1) a co-design methodology for eliciting and encoding journalistic values, (2) a values-aware reranking instantiation built on SCRUF-D, and (3) longitudinal field evaluation via POPROX. This work reimagines recommender systems not merely as engines of engagement, but as accountable infrastructures that uphold democratic values.
Understanding the performance of large-scale recommender systems remains an underexplored challenge, especially for content creators and model developers. The raw engagement signals available to them, such as views and clicks, conflate content quality, model behavior, presentation bias, and audience reach, making it ha...
Chao-Ran Guo, Ding Tong, Ting-Po Lee et al.· 0 citations
This study examines how news engineers and related technical stakeholders perceive and implement personalization and explainability in practice, and provides actionable and practical guidelines for news engineers and researchers on how to adopt explainability methods within a news personalization pipeline.
Jasmin Kareem, Siddharth Mehrotra, M. Willemsen et al.· Proceedings of the 20th ACM...· 0 citations
It is found that LLM judges are influenced by descriptions of a recommendation algorithm’s optimization objective, even when evaluating identical recommendation outputs, a phenomenon the authors term intent-description anchoring bias.
Himan Abdollahpouri, Kyle Kretschman, M. Lalmas· Proceedings of the 20th ACM...· 0 citations
Real-world embodiments of recommender systems are increasingly often posed as decision-making systems, imposing consequences on the world around them. The literature on causal and counterfactual inference allows us to reason about these consequences, and better understand their implications. Whilst this research area h...
Olivier Jeunen, Harrie Oosterhuis, Flavian Vasile et al.· Proceedings of the 20th ACM...· 0 citations
Misinformation is widely reported to propagate faster on engagement-based platforms, yet prior work largely focused on empirical analysis, without identifying a specific algorithmic mechanism that results in this phenomenon. Thanks to the open-sourcing of X's recommendation algorithms, we conduct what is, to our knowle...
Recommender systems (RSs) can produce useful outcomes, but they can also cause harm. This raises a basic operational question for responsible recommendation: under what conditions should a system withhold the action it would otherwise select? We define justified non-action as withholding this action in favor of an alte...
Julia Neidhardt, T. Kolb, Ahmadou Wagne et al.· Proceedings of the 20th ACM...· 0 citations
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