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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
Ediciones:
33
ICORE: A   QUALIS: B1   Vistas: 39537   Seguidores: 12   Asistentes: 1

Índice Conference Partner (CP-I)

71,0 / 100
Puesto n.º 321 de 5.687 congresos · 6% superior
Reconocimiento académico (35%)
88
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
95
Atención de la comunidad (10%)
37
Integridad del registro público (15%)
50

Datos utilizados: Categorías: ICORE A, QUALIS B1 · Ediciones documentadas: 33 · Investigadores que lo siguen aquí: 12 · Investigadores que abrieron esta página en los últimos 24 meses: 4

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Ediciones anteriores (+3,0)
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Confianza 80 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-25

Solicitud de Artículos

UMAP 2025 (International Conference on User Modeling, Adaptation, and Personalization) is a ICORE A / 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
Última actualización por Dou Sun el

Los Mejores Artículos

AñoLos Mejores Artículos
2025Simulating Human Opinions with Large Language Models – Opportunities and Challenges for Personalized Survey Data Modeling
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 Reinforcement Learning and Spatial Proximity Exploration for New User and New POI Recommendations
2023Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware 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
2020A Stable Personalised Partner Selection for Collaborative Privacy Education
2020Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations
2020Personalized Recommendation of PoIs to People with Autism
2019One Size Does Not Fit All: Badge Behavior in QandA Sites
2019Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
2018Easy to Please: Separating User Experience from Choice Satisfaction
2018Intent-aware Item-based Collaborative Filtering for Personalised Diversification
2018Predict Demographic Information Using Word2vec on Spatial Trajectories
2017Experimental Analysis of Mastery Learning Criteria
2016On the Value of Reminders within E-Commerce Recommendations
2016Reinforcement Learning: the Sooner the Better or the Later the Better?
2016Gender Differences in Facial Expressions of Affect During Learning
2016Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique
2016Automatic Teacher Modeling from Live Classroom Audio
2016Analyzing and Predicting Task Reminders
2016Predicting Individual Differences for Learner Modeling in Intelligent Tutorsfrom Previous Learner Activities
2015The Mars and Venus Effect: The Influence of User Gender on the Effectiveness of Adaptive Task Support
2014Toward Fully Automated Person-Independent Detection of Mind Wandering
2014Adaptive support versus alternating worked examples and tutored problems: Which leads to better learning?
2014Te,Te,Hi,Hi: Eye Gaze Sequence Analysis for Informing User-Adaptive Information Visualizations
2013Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation
2012Users and Noise: Estimating the Magic Barrier of Recommendation Systems
2011Analyzing User Modeling on Twitter for Personalized News Recommendations
2010Analysis of Strategies for Building Group Profiles
2009Tell Me Where You've Lived, And I'll Tell You What You Like: Adapting Interfaces to Cultural Preferences
2008Social Information Access for the Rest of Us: An Exploration of Social YouTube
2007Exploiting Evidence Analysis in Plan Recognition

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CCFNombre CompletoFactor de ImpactoEditorISSN
BUser Modeling and User-Adapted Interaction3.5Springer0924-1868
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CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
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