User Modeling and User-Adapted Interaction Template
Write in a clean editor, then format for User Modeling and User-Adapted Interaction in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the User Modeling and User-Adapted Interaction format
User Modeling and User-Adapted Interaction is a peer-reviewed journal published by Springer Nature, covering Recommender Systems and Techniques, Intelligent Tutoring Systems and Adaptive Learning, Speech and dialogue systems.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. User Modeling and User-Adapted Interaction 12, 45–58 (2023).
Formats any DOI in User Modeling and User-Adapted Interaction style. No sign-up. |
| Publishes research in | Recommender Systems and Techniques Intelligent Tutoring Systems and Adaptive Learning Speech and dialogue systems Innovative Teaching and Learning Methods Topic Modeling |
| ISSN | 0924-1868 |
| Citation impact (2-yr) | 6.5 |
| h-index | 106 |
| i10-index | 442 |
| Total citations | 48,291 |
| Article processing charge | $2,990 |
| Top institutions publishing here | Carnegie Mellon University |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in User Modeling and User-Adapted Interaction per year
Citation impact of User Modeling and User-Adapted Interaction by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in User Modeling and User-Adapted Interaction
Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative…