No. 8 | Editor: Elisabeth André | July 30, 1999 |
News |
Welcome to No. 8 of the ETAI Newsletter. It contains the following contributions:
Paper Discussion Ehud Reiter sent me some comments on the article: Knut Hartmann, Bernhard Preim and Thomas Strothotte: Describing Abstraction in Rendered Images through Figure Captions Please click here to find Ehud comments as well as the authors' response. The paper is under confidential review now. But of course further contribution to the open discussion are still very welcome.
Book Review: Annika Waern sent me some follow-up comments on Anthony Jameson's review for the book: Adaptive Hypertext and Hypermedia, edited by Peter Brusilovsky, Alfred Kobsa, and Julita Vassileva. You may find them under the book's discussion page. Further comments would be appreciated very much. Please send them to andre@dfki.de.
Best regards,
Elisabeth André
(area editor)
Book Reviews |
What Can the Rest of Us Learn From Research on Adaptive Hypermedia - and Vice-Versa?Comments by Anthony Jameson on the book Adaptive Hypertext and Hypermedia, edited by Peter Brusilovsky, Alfred Kobsa, and Julita Vassileva (Dordrecht: Kluwer, 1998) |
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No | Comment(s) | Answer(s) | Continued discussion |
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22.3.99 Anthony Jameson |
29.3.99 Annika Waern |
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Comments by Anthony Jameson on the book Adaptive Hypertext and Hypermedia, edited by Peter Brusilovsky, Alfred Kobsa, and Julita Vassileva (Dordrecht: Kluwer, 1998).
My intent with these comments is not to discuss each individual article in this collection; this task was performed well by the three editors in their Preface, and Peter Brusilovsky has kindly made this preface available on-line for the purpose of this discussion.
Nor will I offer an overview of the methods and techniques of adaptive hypermedia, since that job was done by Peter Brusilovsky in the thorough and insightful review article that begins this book. I can simply advise anyone who is at all interested in this area to acquire and read this at least this overview article, even if they for some reason can't manage the whole book.
Note that the recommendation is to read and use the overview, not just to cite it. As the author of an earlier overview article in the journal in which this one originally appeared, I should warn that overview articles threaten to diminish the extent to which researchers are aware of relevant previous work - although, of course, they have the purpose and the potential of doing the opposite. The problem is that some authors think they can fulfill their obligation to cite "related work" by citing the overview article. So they don't need to read any of the previous works themselves - or even the overview article. Fortunately, the use of this strategy is usually betrayed by the presence of recognizably false statements in the work of the authors who use it (e.g., that they are the first ones in the history of the planet who have used a particular technique).
My aim with these comments is to place the work in this book in the broader context of work on user-adaptive systems, which of course encompasses research involving a lot of systems other than adaptive hypermedia systems: What can those who work on other types of user-adaptive systems learn from research on adaptive hypermedia--and what can adaptive hypermedia researchers perhaps learn from the others?
To this end, I've indexed the 7 system-specific papers in the book (i.e., all of them except Brusilovsky's overview) according to a general scheme that has proven useful for integrating the many lines of research in the broad area of user-adaptive systems. (For an application of this scheme to the whole area, see the online proceedings of UM97, the Sixth International Conference on User Modeling, which are accessible from sites in the U.S. and in Germany.)
We'll consider in turn the following questions, which can be asked about any
user-adaptive system:
Several general purposes of adaptation can be distinguished. The following table
shows the purposes that are served by the systems represented in the book.
(For each system, the authors of the article are listed; for more information about
the article, see the Preface.) The table also lists the general
purposes that are sometimes served by other user-adaptive systems, though not by any
systems in this book.
Not surprisingly, we see that in
adaptive hypermedia systems, the goals of helping users to access information and presenting
information in an appropriate way are in the foreground. But several examples also
show that similar methods are applied in systems whose main purpose is
different. Those who are working on types of systems not represented in this book
may want to consider adopting adaptive hypermedia techniques to serve particular
functions within their systems. For example, though the main emphasis in a system
for supporting collaboration may be on choosing appropriate collaborators
for U, adaptive hypermedia could be used for presenting information about the
collaborators or the task effectively.
Adaptive hypermedia systems, more frequently than other types, model the current task
or goal of the user, sometimes after having requested an explicit specification of it (see
below). This property has fairly obvious relevance when it comes to deciding what
information to supply or what links to recommend.
But designers of other types of adaptive systems may also get
some ideas by looking at the ways in which this aspect of a user model is acquired
and put to use
in adaptive hypermedia systems.
Assessments of U's general level of domain knowledge likewise have fairly obvious
uses in such systems, but they are less unique to adaptive hypermedia, being used in
many other types of adaptive system as well.
Adaptive hypermedia systems use about every possible type of data about the user
as evidence on which to base their user models. But on the whole, explicit
elicitation of information about U seems to be relatively more frequent here than
in other areas of user modeling. As several of the authors note, many of the
naturally occurring actions of hypermedia users (e.g., selection of particular
links; time spent on a particular screen) are hard to interpret. On the whole, the
emphasis in this area is more on clever ways of adapting hypermedia, given
particular beliefs about the user, and less on sophisticated methods for deriving
such beliefs on the basis of indirect evidence.
One is struck here by the complete absence of general AI techniques for
uncertainty management and machine learning that have gained increasing prominence
in connection with other types of user-adaptive systems.
By contrast, stereotype-based methods for classifying users are strongly represented in this
collection, relative to the field of user-adaptive systems as a whole (though work
on the three systems that use these methods was completed by the early
1990s). Particular user actions (or combinations thereof) trigger the activation of
associated stereotypes, though these inferences would be hard to justify
theoretically.
Specifically developed formulas and rules, which are independent of any general
framework, may constitute the best inference methods when the inferences to be made
are straightforward and deterministic. But where
noisy data or uncertainty are involved, why not leverage the powerful techniques
that have been developed to deal with this sort of inference problem? Arguments are
found at several points in this collection against the use of "unnecessarily complex
and unwieldy" inference methods. But such arguments remain unconvincing in the light
of (a) the demonstrated utility of powerful AI techniques in connection with other
types of user-adaptive systems and (b) the lack of any attempt to apply such
techniques or even to consider how they might be applied.
Relevant examples of the application of such techniques include the various
types of recommender systems that have been developed in recent years - even though
most of these do not recommend hypermedia documents.
Here again, we see an emphasis on relatively specific insights about appropriate
adaptation, as opposed to general methods for making adaptation decisions. In
connection with the exploitation of an existing user model,
Bayesian and machine learning methods could be used for predicting the user's
reactions to a given system action (e.g., whether U would understand or enjoy a
given screen that S might present). Decision-theoretic methods could be used for
the systematic evaluation of possible system actions.
Although Brusilovsky, in his overview, is rather pessimistic about the current empirical
basis of research into adaptive hypermedia, the articles in this collection do
contain some impressive examples along these lines. For example, Höök et al. and
Vassileva went to unusual lengths, in an early stage of system design, to study
potential users' work habits and needs so as to arrive at
a realistic set of system requirements. When reading these accounts, one wonders
how the system in question would ultimately have been received by users if these
preliminary studies had not been carried out.
Another aspect of this research that deserves imitation is represented by the
several thorough experimental studies of system prototypes. These studies did not
merely check whether the new system was better than a more traditional alternative;
they also yielded rich information about the strengths and limitations of the
approaches taken.
The research represented in this collection includes many relatively specific
insights about ways in which a hypermedia system can adapt its behavior given
particular assumptions about properties of the user. The demonstrated effectiveness
of some of these systems can be seen as proof that technical creativity and
attention to the requirements of a particular application scenario can produce
usefully adaptive systems, even in the absence of powerful inference techniques or a
general theoretical foundation.
At the same time, it would seem advisable to devote increased attention in this
area to machine learning and uncertainty management techniques. The reliance of the
systems on explicit user input - which users are in many contexts reluctant or
unable to
provide accurately - could be reduced, and the systems' behavior could become easier to justify
and to explain.
Needless to say, readers may disagree with the opinions expressed in these
comments. To encourage discussion, I hereby offer to send my shiny new copy
of this book, which was supplied to me for the purpose of this discussion (but which
I don't need, having already possessed all of the articles), to the person
who offers the most interesting contribution to this discussion, in the judgment of the
newletter's editor, Elisabeth André, by April 15th, 1999.
In what way is the system S's adaptation to the user U intended to be beneficial
to U?
What sort of information about U is represented in S's
user model?
On the basis of what types of evidence does S construct its user
model?
According to what principles or inference techniques does S
arrive at the hypotheses about U that are stored in the user
model?
According to what principles or inference techniques does S
decide how to adapt its behavior on the basis of the information in its user model?
What sorts of empirical data give us reason to believe that S's
methods are valid and useful?
Purposes of Adaptation
Indexing of System-Specific Articles
Comments
Properties of users represented
Indexing of System-Specific Articles
Comments
Input for user model construction
Indexing of System-Specific Articles
Comments
Methods for constructing the user model
Indexing of System-Specific Articles
Methods for exploiting the user model
Indexing of System-Specific Articles
Comments
Empirical foundations
Indexing of System-Specific Articles
Comments
Concluding Remarks