User Modeling and User-Adapted Interaction

Aims & Scope

User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation.

The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.5
2024	3.00

SJR (SCImago Journal Rank)

Year	Value
2024	0.759

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	87

Journal Rank

Year	Value
2024	7272

Journal Citation Indicator

Year	Value
2024	454

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Hybrid Recommender Systems: Survey and Experiments

  Citation: 2620

  Authors: Robin

• Knowledge tracing: Modeling the acquisition of procedural knowledge

  Citation: 1277

  Authors: Albert T., John R.

• Adaptive Hypermedia

  Citation: 938

  Authors: Peter

• Methods and techniques of adaptive hypermedia

  Citation: 825

  Authors: Peter

• Explaining the user experience of recommender systems

  Citation: 558

  Authors: Bart P., Martijn C., Zeno, Hakan, Chris

• Recommender systems: from algorithms to user experience

  Citation: 536

  Authors: Joseph A., John

• User Modeling in Human–Computer Interaction

  Citation: 487

  Authors: Gerhard

• Generic User Modeling Systems

  Citation: 328

  Authors: Alfred

• User Modeling for Adaptive News Access

  Citation: 321

  Authors: Daniel, Michael J.

• The effects of transparency on trust in and acceptance of a content-based art recommender

  Citation: 320

  Authors: Henriette, Vanessa, Satyan, Maarten, Lloyd, Natalia, Lora, Bob

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