Methods and techniques of adaptive hypermedia
Abstract
Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.
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- Authors
- Brusilovsky, P.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Brusilovsky, Peter
- ORCID
- Brusilovsky, Peter
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 1996-07
- Volume
- 6
- Issue
- 2-3
- Pages
- 87-129
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W2112929667
- Cited by
- 2,041
- References
- 68
- Citations by year
- 2026: 19 · 2025: 11 · 2024: 13 · 2023: 24 · 2022: 18 · 2021: 23 · 2020: 44 · 2019: 33 · 2018: 41 · 2017: 50 · 2016: 57 · 2015: 58 · 2014: 68 · 2013: 71 · 2012: 94
- FWCI
- 45.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- User Modeling and User-Adapted Interaction (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Web Applications and Data Management
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- adaptive hypermedia, user modeling, content adaptation, link recommendation
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