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).

My intent with this 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.

Instead, my aim 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:


  1. Purposes of adaptation
    In what way is the system S's adaptation to the user U intended to be beneficial to U?
  2. Properties of users represented
    What sort of information about U is represented in S's user model?
  3. Input for user model acquisition
    On the basis of what types of evidence does S construct its user model?
  4. Methods for constructing the user model
    According to what principles or inference techniques does S arrive at the hypotheses about U that are stored in the user model?
  5. Methods for exploiting 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?
  6. Empirical foundations
    What sorts of empirical data give us reason to believe that S's methods are valid and useful?

Back to overview

Purposes of Adaptation

Indexing of System-Specific Articles

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.

Comments

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.

Back to overview

Properties of users represented

Indexing of System-Specific Articles

Comments

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 adaptive systems may also get some ideas by looking at the ways in which this aspect of a user model is 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.

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Input for user model construction

Indexing of System-Specific Articles

Comments

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.

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Methods for constructing the user model

Indexing of System-Specific Articles

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 in 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. Particularly relevant examples of the application of such techniques are the various types recommender systems that have become prominent in recent years - even though most of these do not recommend hypermedia documents.

Back to overview

Methods for exploiting the user model

Indexing of System-Specific Articles

Comments

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.

Back to overview

Empirical foundations

Indexing of System-Specific Articles

Comments

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 a 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 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.

Concluding Remarks

The research represented in this collection includes many relatively specific insights about ways in which an 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 rich 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 to provide - 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.