Dear all, We excited to announce the first call for contributions for NORMalize: the Workshop on the Normative Design and Evaluation of Recommender Systems! This will take place in the period of September 22-26 in Prague, co-located with the ACM conference on recommender systems (RecSys) We solicit early-stage research ideas and previously published manuscripts from journals or conferences outside the core recommender systems community. More details can be found here: https://sites.google.com/view/normalizeworkshop/recsys25/call-for-papers?aut... Deadline for submissions: July 10, 2025 On behalf of all organizers, Kind regards, Alain Starke Topics of Interest We are especially interested in contributions related to norms and values in recommender systems, including (but not limited to) the following themes: Philosophical, Legal, and Conceptual Foundations * Which norms and values are important in a specific domain, and why? * How should competing norms and values be balanced? * What norms and values are required or influenced by legal frameworks? * How will current and proposed legal frameworks impact the work on recommender systems? Qualitative and Stakeholder-Centered Approaches * How do different stakeholder groups perceive and prioritize norms and values? * What tensions emerge between stakeholder expectations and system design? Value-Sensitive and Normative Algorithm Design * How can we operationalize abstract norms and values into algorithmic objectives? * How can algorithms be designed to optimize for or balance multiple values? * What frameworks support multi-objective, multi-stakeholder optimization? Metrics and Evaluation Methods * How can we measure norms and values in recommender systems? * What kinds of data representations are required for such measurements? * Do value-oriented metrics generalize across domains? * How can we design robust experiments that surface normative dimensions? Datasets and Data Practices * How can public datasets support research on norms and values? * How does data representation affect value-sensitive evaluation? Case Studies and Empirical Insights * What norms and values emerge in real-world deployments? * What practical challenges arise in implementing value-sensitive systems? * How do deployed recommender systems behave with respect to these values?