Dear all, We are pleased to share the preliminary call for papers for the ACM conference on Recommender Systems, which will take place in Prague, Czech Republic, September 22-26, 2025. It is available on our website: https://recsys.acm.org/recsys25/call/. We invite Full paper and Short paper submissions This year includes a revised list of topics of interest to also cater to a diverse group of communities. At the end of the email is the current list, but this may be subject to change. Preliminary deadlines: FULL PAPERS Abstract submission deadline: April 1, 2025 Paper submission deadline: April 8, 2025 Author rebuttal period: May 20-26, 2025 Author Notification: July 3, 2025 Camera-ready version deadline: July 21, 2025 SHORT PAPERS Abstract submission deadline: April 22, 2025 Paper submission deadline: April 29, 2025 Author rebuttal period: June 5-10, 2025 Author Notification: July 3, 2025 Camera-ready version deadline: July 21, 2025 On behalf of the ACM RecSys organizing committee, Kind regards, Alain Starke Relevant Areas and Topics Relevant research areas and topics of interest for RecSys 2025 include but are not limited to: * HUMAN-CENTERED RECOMMENDATION APPROACHES * Adaptive algorithms that adjust recommendations dynamically based on user interactions * Explanation methods and interfaces for recommender systems * Human-in-the-loop model learning and validation * Innovative user interfaces for recommender systems * Novel interaction paradigms * Novel methods for improving the explainability of recommendation models * Novel methods for preference elicitation * Novel perspectives on the role of transparency in recommender systems * Studies that investigate how real-time events (e.g., trends and holidays) influence user preferences * User control of recommender systems * User experiments and studies of recommendation applications * SOCIETAL RECOMMENDER SYSTEMS * Adapting recommendation algorithms to suit different cultural contexts and multilingual users * Addressing global digital divides by exploring the design, development and deployment of systems for low-resource environments * Bias, fairness, bubbles, and ethics of recommender systems * Diversity and inclusion models * Eco-aware recommendation models * Ensuring equitable access to digital resources for understudied populations * Implications of recommendation algorithms when the main stakeholders are understudied populations like older adults, young children and adolescents, individuals afflicted by mental health disorders, users with autism spectrum disorder (ASD), individuals with intellectual disabilities, or those with specific learning needs * Recommendation models for sustainable tourism * Studying the effects of recommendation systems on cultural and global diversity lenses * Supporting understudied communities by elevating diverse and underrepresented content * Sustainable Recommender Systems Development * COMPUTATIONAL INNOVATIONS, EVALUATION, AND REAL-WORLD APPLICATIONS * Building large-scale, standardized datasets for benchmarking algorithms beyond the traditional movie and music domains * Case studies of real-world implementations * Conversational and natural language recommender systems * Cross-domain recommendation * Design of usability studies * Economic models and consequences of recommender systems * Generative models in recommender systems * Legal and regulatory aspects of recommender systems * Knowledge-based recommender systems * Multimodal approaches for recommendation * Multi-stakeholder recommendations * Novel evaluation metrics beyond accuracy * Privacy and security * Recommendation models for education and learning-related technologies * Studies that investigate the relationship between evaluation metrics and real-world outcomes * Tailoring recommendations for applications beyond e-commerce, e.g. healthcare, food recommendations, well-being, education, personalized financial services, etc.