Información de la conferencia

UMAP 2025: International Conference on User Modeling, Adaptation, and Personalization

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Día de Entrega:
2025-01-23
Fecha de Notificación:
2025-03-20
Fecha de Conferencia:
2025-06-16
Ubicación:
New York, USA
Años:
33
CORE: b   QUALIS: b1   Vistas: 36200   Seguidores: 12   Asistentes: 1

Solicitud de Artículos

UMAP 2025 (International Conference on User Modeling, Adaptation, and Personalization) is a CORE B / QUALIS B1 conference held in New York, USA on 2025-06-16. The paper submission deadline is 2025-01-23. Acceptance notifications are sent on 2025-03-20.

ACM UMAP is the premier international conference bringing together research in AI and HCI to support effective human-AI collaboration via interactive systems that can model, adapt and personalize to their users. The conference is sponsored by ACM SIGCHI and SIGWEB. User Modeling Inc., as the core Steering Committee, oversees the conference organization. UMAP operates under the ACM Conference Code of Conduct. ACM UMAP 2025 will bring together researchers, practitioners, and policymakers from around the world to discuss the latest developments in human-centered approaches to design and use AI systems that adapt to human needs while ensuring user control, fairness, accountability, transparency, inclusivity and sustainability. The accepted papers will be published in the conference proceedings, accessible through the ACM Digital Library. Topics of interest include but are not limited to Recommender systems Data mining techniques for user modeling, adaptation, and personalization Generative AI techniques for user modeling, adaptation, and personalization Large Language Models and Natural Language Processing methods for user modeling, adaptation and personalization Knowledge graphs, Linked data, and semantics for user modeling, adaptation, and personalization Intelligent user interfaces Intelligent and personalized e-learning applications and educational games Personalized behavior change and persuasive applications Modeling and adapting to human affective states Virtual assistants, conversational agents, personalization in augmented reality Group modeling and collaborative team formation Ethical issues of personalization and human-centered AI systems: Privacy, Fairness, Accountability, Transparency Creativity in User Modeling, Adaptation and Personalization Sustainability-aware methods and Sustainable Development Goals (SDGs) for user modeling, adaptation, and personalization Personalized approaches for preventing eco-chambers, user manipulation, and disinformation Research methods and reproducibility
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Los Mejores Artículos

AñoLos Mejores Artículos
2025“Strangers in a new culture see only what they know”: Evaluating Effectiveness of GPT-4 Omni for Detecting Cross-Cultural Communication Norm Violations
2025Legal but Unfair: Auditing the Impact of Data Minimization on Fairness and Accuracy Trade-off in Recommender Systems
2025Synthetic Voices: Evaluating the Fidelity of LLM-Generated Personas in Representing People’s Financial Wellbeing
2024Integrating sentiment features in factorization machines: Experiments on music recommender systems
2024User Perceptions of Diversity in Recommender Systems
2024Explaining the Unexplainable: The Impact of Misleading Explanations on Trust in Unreliable Predictions for Hardly Assessable Tasks
2023Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware Recommendations
2023Combining Reinforcement Learning and Spatial Proximity Exploration for New User and New POI Recommendations
2022Walking with PACE – Personalized and Automated Coaching Engine
2022How to Ask for Donations? Learning User-Specific Persuasive Dialogue Policies through Online Interactions
2021Generating Interesting Song-to-Song Segues With Dave
2021Data-Driven Modeling of Learners' Individual Differences for Predicting Engagement and Success in Online Learning
2020Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations
2020Personalized Recommendation of PoIs to People with Autism
2020A Stable Personalised Partner Selection for Collaborative Privacy Education
2019Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
2019One Size Does Not Fit All: Badge Behavior in QandA Sites
2018Intent-aware Item-based Collaborative Filtering for Personalised Diversification
2018Easy to Please: Separating User Experience from Choice Satisfaction
2018Predict Demographic Information Using Word2vec on Spatial Trajectories
2017Experimental Analysis of Mastery Learning Criteria
2016Predicting Individual Differences for Learner Modeling in Intelligent Tutorsfrom Previous Learner Activities
2016On the Value of Reminders within E-Commerce Recommendations
2016Analyzing and Predicting Task Reminders
2016Automatic Teacher Modeling from Live Classroom Audio
2016Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique
2016Gender Differences in Facial Expressions of Affect During Learning
2016Reinforcement Learning: the Sooner the Better or the Later the Better?
2014Te,Te,Hi,Hi Eye Gaze Sequence Analysis for Informing User-Adaptive Information Visualizations
2014Toward Fully Automated Person-Independent Detection of Mind Wandering
2014Adaptive support versus alternating worked examples and tutored problems: Which leads to better learning?

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Revistas Relacionadas

CCFNombre CompletoFactor de ImpactoEditorISSN
bUser Modeling and User-Adapted Interaction3.5Springer0924-1868
bIEEE Transactions on Multimedia9.7IEEE1520-9210
cKnowledge-Based Systems7.2Elsevier0950-7051
bSoftware & Systems Modeling3.2Springer1619-1366
aIEEE Transactions on Computers3.8IEEE0018-9340
cFuture Generation Computer Systems6.1Elsevier0167-739X
cNeurocomputing6.5Elsevier0925-2312
cPattern Recognition Letters3.9Elsevier0167-8655
IEEE Access3.6IEEE2169-3536
bPattern Recognition7.6Elsevier0031-3203

AsistenteAño
J.C. Yao (693)2016
Publicidad