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User Modeling and User Adapted Interaction, 1996, v 6, n 2-3, pp 87-129
(Special issue on adaptive hypertext and hypermedia)
Methods and techniques of adaptive hypermedia
Peter Brusilovsky
HCII, School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213.
E-mail: plb+@andrew.cmu.edu
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.
Keywords: adaptive hypermedia, navigation support, collaborative user
modeling, adaptive text presentation, intelligent tutoring systems, student
models.
1 Introduction
Hypermedia systems have become increasingly popular in the last five years as tools for user-
driven access to information. Adaptive hypermedia is a new direction of research within the area of
user-adaptive systems. The goal of this research is to increase the functionality of hypermedia by
making it personalized. Adaptive hypermedia (AH) systems build a model of the goals,
preferences and knowledge of the individual user and use this throughout the interaction for
adaptation to the needs of that user.
AH systems can be useful in any application area where the system is expected to be used by
people with different goals and knowledge and where the hyperspace is reasonably big. Users
with different goals and knowledge may be interested in different pieces of information presented
on a hypermedia page and may use different links for navigation. AH tries to overcome this
problem by using knowledge represented in the user model to adapt the information and links
being presented to the given user. Adaptation can also assist the user in a navigational sense,
which is particularly relevant for a large hyperspace. Knowing user goals and knowledge, AH
systems can support users in their navigation by limiting browsing space, suggesting most
relevant links to follow, or providing adaptive comments to visible links. The goal of this paper is
to provide an overview of recent work on the development of adaptive hypermedia systems.
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Since this area of research is very new, the concept of adaptive hypermedia systems has not
been clearly defined yet. To make the scope of the review more clear we use in this paper the
following working definition:
by adaptive hypermedia systems we mean all hypertext and hypermedia systems
which reflect some features of the user in the user model and apply this model to
adapt various visible aspects of the system to the user.
In other words, the system should satisfy three criteria: it should be a hypertext or hypermedia
system, it should have a user model, and it should be able to adapt the hypermedia using this
model (i.e. the same system can look different to the users with different models). We have
identified more than 20 systems which can be named as adaptive hypermedia systems according to
our criteria (Appendix 1). The analysis of these systems is the main content of our review. Note
that not all known systems which are named or referred to as adaptive hypermedia satisfy our
definition. Some of them are not full-fledged hypermedia systems (Brusilovsky, 1992b; Yetim,
1993; André & Rist, 1996); some of them are not really adaptive, but rather adaptable
(Waterworth, 1996) (this distinction will be made clearer later). There are also some projects
which suggest interesting relevant ideas but have not yet reached the implementation stage
(Tomek, Maurer & Nassar, 1993; Zyryanov, 1996). All these works, however, contain interesting
ideas and we refer to them when it is relevant to the main line of presentation.
System
Processes
User Model
Collects
Data about user
Adaptation effect
User Modeling
Adaptation
Processes
Figure 1. Classic loop "user modeling - adaptation" in adaptive systems
In this paper, the critical feature of adaptive hypermedia systems is the possibility of providing
hypermedia adaptation on the basis of the user model. Therefore, the paper is centered around the
problems of adaptation, the second part of the overall adaptation process in adaptive computer
systems (Figure 1). The main content of the paper (sections 2-6) is a review of existing methods
and techniques of adaptation in AH systems. The problems of user modeling, i.e. building and
updating the user model in AH systems, are not a focus of the paper because they are not as critical
for AH systems as a subclass of adaptive computer systems. Specific problems of user modeling
in AH systems are discussed in section 7 which provides a comparative review of several methods
of user modeling in AH systems. Special attention is paid to collaborative user modeling which are
especially important for this AH system. The conclusion summarizes the content of the paper and
discusses the prospects for research in the area of adaptive hypermedia.
2 Methods and techniques of adaptive hypermedia
Adaptation techniques refers to methods of providing adaptation in existing AH systems.
These techniques are a part of the implementation level of an AH system. Each technique can be
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characterized by a specific kind of knowledge representation and by a specific adaptation
algorithm. Adaptive hypermedia is a new area of research and most of the adaptation techniques
are still unique in the sense that each of them was suggested in conjunction with the development
of an AH system. However, some popular techniques were already implemented with minor
variants in several earlier systems.
Adaptation methods are defined as generalizations of existing adaptation techniques. Each
method is based on a clear adaptation idea which can be presented at the conceptual level. For
example, "...insert the comparison of the current concept with another concept if this other concept
is already known to the user", or "...hide the links to the concepts which are not yet ready to be
learned". The same conceptual method can be implemented by different techniques. At the same
time, some techniques are used to implement several methods using the same knowledge
representation.
The set of methods and techniques forms a tool kit or an "arsenal" of adaptive hypermedia and
can be used as a source of ideas for the designers and developers of adaptive hypermedia systems.
Techniques,
implementation level
(section 6)
Methods,
conceptual level
(section 6)
Adaptation goals
(sections 3 & 6)
Adaptation
technologies
(section 5)
Application areas
(section 3)
Systems
(appendix 1)
To what?
User features
(section 4)
Where?
Why?
How? What?
How?
Figure 2. Possible classifications for AH methods and techniques.
An arrow stands for 1-to-N relationship.
To review AH systems it is first necessary to establish the basis for the classification of
adaptive hypermedia methods and techniques (Figure 2). The identified dimensions are quite
typical for the analysis of adaptive systems in general (Dieterich et al., 1993).
• The first dimension considered is where adaptive hypermedia systems can be helpful. The
review identifies several application areas for AH systems (see Table 1) and for each area
points the problems which can be partly solved by applying adaptive hypermedia techniques
(section 3).
• The second dimension is what features of the user are used as a source of the adaptation, i.e.
to what features of the user the system can adapt its behavior. The review identifies several
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user features which are considered important by existing AH systems and discusses the
common ways to represent them (section 4).
• The third dimension is what can be adapted by a particular technique. Which features of the
system can be different for different users. Along this dimension the review identifies seven
ways to adapt hypermedia (see Figure 4). They can be divided into two essentially different
groups - content adaptation and link adaptation (section 5). I call different ways to adapt
hypermedia technologies of adaptation.
• The fourth dimension of classification is the adaptation goals achieved by different methods
and techniques: why these methods and techniques are applied, and which problems of the
users they can solve. The adaptation goals are dependent on application areas. Each application
area has its own set of problems and each goal is important in some range of application areas
(section 3). The adaptation goals are considered in parallel with reviewing of relevant
adaptation methods and techniques which implement these methods (section 6).
The four identified dimensions are very suitable to classify various application methods.
Usually, each method is an application of a particular adaptation technology (such as text
adaptation or hiding of links) to achieve one of possible adaptation goals using one of the users
features as a source for adaptation (As an exception, methods can achieve more than one goal or
use more than one feature of the user). According to its goals and used features a particular method
can be useful in a subset of application areas.
3 Where and why adaptive hypermedia can be helpful
Unlike other kinds of application systems, any hypermedia system is adaptive in some sense:
using free browsing different users can adapt the system to their information needs. Many
researchers hold that it is the user who should bring the adaptivity to the man-machine hypermedia
system. Why do we need any other kind of adaptation? Why do we need that a hypermedia system
adapts itself to the particular user? The answer depends on an application area perspective.
Analysis of existing AH systems allow us to name six kinds of hypermedia systems which are
used at present as application areas in most of research projects on adaptive hypermedia. These
are: educational hypermedia, on-line information systems, on-line help systems, information
retrieval hypermedia systems, institutional information systems, and systems for managing
personalized views (Table I). In each of these areas adaptive hypermedia techniques can be helpful
because they help solve the identified problems. This section characterizes all these application
areas, pointing out their specific features and identifying problems.
Educational Hypermedia
Systems
Anatom-Tutor, C-Book, <Clibbon>, ELM-ART, ISIS-Tutor,
ITEM/PG, HyperTutor, Land Use Tutor, Manuel Excel,
SHIVA, SYPROS, ELM-PE, Hypadapter, HYPERCASE
On-line Information
Systems
Hypadapter, HYPERCASE, KN-AHS, MetaDoc, PUSH,
HYPERFLEX, CID, Adaptive HyperMan
On-line Help Systems EPIAIM, HyPLAN, Lisp-Critic, ORIMUHS, WING-MIT,
SYPROS
Information Retrieval
Hypermedia
CID, DHS, Adaptive HyperMan, HYPERFLEX, WebWatcher
Institutional Hypermedia Hynecosum
Personalized Views Basar, Information Islands
Table I. Existing adaptive hypermedia systems classified according their application areas. Second entries for the
systems that fit two categories are shown in italics.
Bibliographic references are provided in Appendix 1.
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The most popular area for adaptive hypermedia research is educational hypermedia. Existing
educational hypermedia systems have relatively small hyperspaces representing a particular course
or section of learning material on a particular subject. The goal of the student is usually to learn all
this material or a reasonable part of it. The hypermedia form supports student-driven acquisition of
the learning material. The most important user feature in educational hypermedia is user
knowledge of the subject being taught. Adaptive hypermedia techniques can be useful to solve a
number of the problems associated with the use of educational hypermedia. Firstly, the knowledge
of different users can vary greatly and the knowledge of a particular user can grow quite fast. The
same page can be unclear for a novice and at the same time trivial and boring for an advanced
learner. Second, novices enter the hyperspace of educational material knowing almost nothing
about the subject. Most of the offered links from any node lead to the material which is completely
new for them. They need navigational help to find their way through the hyperspace. Without such
a help they can "get lost" even in reasonably small hyperspaces, or use very inefficient browsing
strategies (Hammond, 1989).
Another popular application for adaptive hypermedia is the area of various on-line information
systems from on-line documentation to electronic encyclopedias. The goal of these systems is to
provide reference access to information (rather then a systematic introduction as in educational
hypermedia) for the users with different knowledge level of the subject. Each node of the
hyperspace usually represents one concept of the subject and contains several pages of
information. Depending on the subject, the size of the hyperspace can range from reasonably small
to very large. Similar to educational hypermedia, on-line information systems have problems with
satisfying the needs of very different users. Those with different knowledge and background need
different information about a concept and at different levels of detail. They usually have no time to
browse all the information about the concept to look for the required portion of information. Users
also have different goals when accessing an information system. In some cases they know which
concepts to access to achieve their goals and do not need any navigational support (Boyle &
Encarnacion, 1994; Kobsa, Müller & Nill, 1994). However, when the goal can not be directly
mapped to the structure of the hyperspace or when the hyperspace is large, users need help in
navigation and in finding relevant pieces of information. To provide such help, the system has to
know the user's goal (Höök et al., 1996; Micarelli & Sciarrone, 1996). As we will see later
(section 7) inferring the user's goal is a difficult problem in on-line information systems unless the
goal is provided directly by the user (Höök et al., 1996).
Very close to on-line information systems are on-line help systems. These systems serve on-
line information about computer applications (such as a spreadsheet, programming environment,
or expert system) which is required to help the users this system. The difference from the former
category is that on-line help systems are not independent as on-line information systems but are
attached to their application system. Another difference is that the hyperspace in existing on-line
help systems is reasonably small. As we will see later, the distinction between small and large
hyperspace is important from adaptation point of view, and that gives a reason to distinguish these
application areas. On-line help systems and on-line information systems share the problem of
serving different information to different users. At the same time, the problem of helping users to
find relevant pieces of information is less important for on-line help systems because the
hyperspace is not large and because the system knows the context from which the user called for
on-line help (context-sensitive help). The context of work in an application system provides a
reliable source of information for an adaptive on-line help system to determine the user's goal and
to offer the most relevant help items (Encarnação, 1995b; Grunst, 1993; Kim, 1995).
The three application areas listed above belong to traditional application areas for hypermedia.
The majority of existing hypermedia systems belong to one of these three areas. It is not surprising
that most adaptive hypermedia systems also belong to these areas. The three areas listed below are
more recent application areas for hypermedia.
Information retrieval (IR) hypermedia systems is a new class of IR systems which combine
traditional information retrieval techniques with a hypertext-like access from the index terms to
documents and provide the possibility of browsing the hyperspace of documents using similarity
links between documents (Agosti, Melucci & Crestani, 1995; Helmes, Razum & Barth, 1995). It