Edited Adaptive Hypermedia: Combining Human and Machine Intelligence
to Achieve Filtered Information
To be presented at the
Flexible Hypertext Workshop
held in conjunction with
The Eighth ACM International Hypertext Conference (Hypertext'97)
PostScript version
Abstract
We discuss a novel approach to filtering of
hypermedia information based on an information broker and
user environment coupled together. The advantage of the proposed
approach, edited adaptive hypermedia, is that it combines human
expertise with machine intelligence in order to achieve high quality
of the filtered information provided to the end users.
1. Background and motivation
Lately, adaptive hypermedia has attracted a lot of attention, as a
means to tackle the problems users encounter with information overflow
and navigation through large information spaces and ordinary
hypermedia (Brusilovsky 1996, Höök et al.
1996). Adaptive hypermedia takes into account the fact that users
vary in knowledge, cognitive skills and reasons for searching for
information. By keeping a model of some aspects of user
characteristics the system can adapt to and aid the user to navigate
and filter information.
A major difficulty in producing adaptive hypermedia systems, or
indeed user-adaptive systems in general, lies in structuring the
information in such a way that it will be possible to do
adaptations. The representation must include characterizations of
users that allow for useful adaptations, and the interface must be
structured to allow the underlying system to infer the required
characteristics from user actions at the hypermedia interface. This
problem is most apparent in domains where the information is rapidly
changing or highly unstructured. How could we, for example, analyse
and represent the widespread needs of users of the WWW in such a way
that it would be possible to filter information or adapt navigation to
an individual user? And even if we could, how could we infer those
needs from just observing the user's navigation through the WWW?
Promising approaches to this problem are those where the user
community itself provides the needed structure through their
preferences and actions. The structure can be provided
directly by the user (filling in keywords, setting rules in e.g. email
filters etc). However, according to e.g. Shneiderman (1987), users
will not perform actions that will not render immediate gain to
themselves. In most cases, indirect, automated approaches are to be
preferred, since they do not require any extra actions from the
end user. The system just observes the choices made by users and tries
to infer their underlying goals or learn about their preferences.
Pattie Maes, (1995)
argues that one reason that software agents should be used is that
users sometimes do not want to bother with detailed tasks, and instead
will want to delegate them to a separate agency. For example, we are
usually quite willing to delegate to a car mechanic to fix our car,
even if this means that we do not quite understand what has been fixed
and how it was done.
Most approaches to inferring user preferences are based on the
actions of a single user. A problem with such approaches is that it
will be hard, if not impossible, for the system deal with new
information. The Firefly system,
developed at
MIT Media Lab, takes care of this problem. The preferences of an
individual user are compared to those of the full user society, and
the user is grouped together with others expressing roughly the same
preferences. This way, the system is able to suggest new information
based on the fact that other users with a similar preference pattern
have liked this information.
However, it may not always be the case that the preferences of a
whole group of people will be able to satisfy a particular user's
needs. In fact, that user might be much more interested in what a
single expert would regard as important information, rather than in
the recommendation of a large group of peers. In the general case,
users may want to judge the relevance of a piece of information
based both on quality (the expertise of the recommender(s)) and on
quantity (the number of people recommending it).
There is also the question of trust, as discussed by Maes
(1994).
Experiments (e.g. Bonsall and Joint (1991)) show that users have
difficulties in placing the right level of trust in automatised
systems. Initially, the trust is often too high, but lowers
dramatically once the system makes a single error. It is much easier
to know what to expect if the service is provided by another human
being rather than an automated agent.
Our solution to the above problems is to put the human editor back
in place. We want to combine the skills of professionals with machine
intelligence in order to filter information and get feedback on user
preferences. In particular, we want to focus on the structuring and
authoring of adaptive hypermedia, a problem not much discussed in
literature (Höök 1996). The next section outlines our vision
of such an integrated environment, while the involved problems and
suggestions for their solution are discussed in section three.
2. Vision
Our vision is to put the human editor back in place. An editor, or
information broker, is a person, such as a journalist, publisher,
scientist or librarian, or just a dedicated individual, who collects
and structures information for the benefit of other information users.
Usually, the information broker has specialist knowledge in a subject,
and knows more than others about how to find and evaluate information
on this subject. The information broker will have a more or less clear
picture of what his/her customers (newspaper readers, book readers,
other scientists, etc.) want and will adapt to their needs.
Information brokers collect information from various sources, evaluate
its relative importance and then choose whether to include the
information as it is, disregard it, summarize it, or perhaps rewrite
or illustrate it differently than in the original source. Examples of
the information broker role are professional editors and journalists
that direct their services towards the open public, and managers of
internal or external information within an enterprise.
Information brokers already exist on the web. In many cases, these
services are maintained by dedicated individuals, rather than
proffessional editors. Some examples are
We propose a service infrastructure that builds upon and extends the
information broker scenario as it can be seen on the web today. It
does so through providing support for the development of adaptive
hypermedia: edited adaptive hypermedia.The service involves two
types of actors, information brokers and information users, with their
respective tasks of collecting, adapting, and reading the information.
We suggest a solution where individual user interests and preferences
are stored in user profiles, available both to the information broker
and to the information user. The user profile will be split into one
public and one private part as suggested by (Cook and Kay, 1994).
Information collection and processing is based on clusters of such
profiles. The outgoing information is annotated as to allow for
individual adaptations for the information user. Finally, the
information user's reading behavior is monitored and feedback is
provided to the information broker, again through the user profile. The
resulting architecture is shown in figure 1.
Figure 1. The Service Architecture.
Edited adaptive hypermedia requires that good environments are
provided both for information brokers and for information users. The
information broker needs to be equipped with:
- an integrated environment
for searching for information, utilising a wide variety of tools
including instructable, learning agents for searching, selecting,
restructuring/rewriting and annotating information for information
users
- feedback from information users on their reading pattern,
preferences, and understanding of the information provided.
Whereas the information user can be experienced or inexperienced in
using the service or in using computerised media at all, the
information broker is always an expert user. Information brokers can
learn to use a wide variety of tools, and can acquire advanced
interaction methods for instructing the agents. They can also be
provided with advanced visualisation tools such as
Spotfire (Ahlberg and Shneiderman 1994)
for reviewing the acquired information, both information retrieved
through search, and the feedback information from users.
Information users can of course be provided with the same type of
environment as information brokers, but the advantage of the broker /
user partition is that this is not neccessary. The information user
environment can provide much simplified interaction models, and in
particular, information users will not need to instruct search agents
themselves.
3. Challenges
3.1 User modelling issues
We propose to use user models in two ways in the edited adaptive
hypermedia service. Firstly, user models are used by information
brokers to select and filter out relevant information to the reader
community, and to structure and annotate the distributed
information. Secondly, the information user environment maintains a
model of the individual user to provide useful adaptations in the
distributed information.
These two types of models will interact in complex ways. An obvious
interaction is that the end user environment only can adapt using such
annotations that the information broker has provided. In a closed
information domain, an appropriate selection of annotations can be
decided upon in advance, but this will not be true in general for
information broker services. Instead, brokers must be provided with
feedback on how well the selected annotations worked in practice.
To allow for dynamic restructuring, some information about the
users' profiles and reading patterns must be passed back from the
information user environment to the information broker environment.
This raises important privacy issues: what information can be passed
on, and how do we ensure that users are in control of what information
is distributed about them? This becomes particularly critical if
information brokers, in turn, can exchange information about their
user groups and their reading patterns. We propose to divide the user
model into one part which is private and one that is public, see also
(Cook and Kay, 1994).
The dynamic restructuring of user characteristics and
annotations require that information brokers are provided with useful
tools for rewiewing and restructuring information about users. In
part, these tools can be automatic, but we believe that information
visualization tools will provide useful support in this task.
3.2 An integrated editor environment
The editor environment must provide support for a number of editorial tasks:
- information search and retrieval,
- information visualisation,
- selecting, restructuring and annotating information with metadata keys
- generation of adaptive hypermedia spaces management of user
feedback and user preferences information.
For several of these tasks, useful
commercial or freely available tools are already available (e.g.
Metacrawler,
Letitizia,
Spotfire,
Altavista,
BASAR),
or high quality research is being
conducted, but the editor environment poses high demands on the
integration of several tools so that they all are accessible within
the same environment, and can exchange information with each other. We
aim to provide an open and extendable software architecture, where
different high-quality tools for the editorial tasks can be included.
A fairly novel requirement though, is the need for highly usable
tools for the generation of adaptive hypermedia: editing, structuring,
and annotating it. The problem is quite difficult, if, for example,
the adaptation happens through a stretchtext technique (Brusilovsky,
1996, Boyle and Encarnacio, 1994), the author
must understand the adaptation mechanism in quite some detail before
s/he can enter new information. In our view an authoring tool should
aid the writer to enter text, pictures, etc. while minimising the
requirement on his/her understanding of the system. Otherwise the
cognitive load on the writer will be tremendous (Höök,
1996). The authoring process is difficult enough anyway.
4. Summary and concluding remarks
We have discussed a novel approach to filtering of
hypermedia information based on an information broker and
user environment coupled together. The advantage of the proposed
approach, edited adaptive hypermedia, is that it combines human
expertise with machine intelligence in order to achieve high quality
in the filtered information provided to the end users. A similar
approach to information brokering is taken in the COBRA project, but
they do not include feedback from the information users.
This approach solves a number of problems related to automated
filtering of information:
- Trust:
- the delegation of tasks to a software agent requires that the user
trusts that the agent will do the right job. In the information broker
service, the usage of a human editor will allow users to trust the
system at the level they trust the professional editor responsible for
the service.
- Novel information:
- using a software agent for search or filtering of information will
face problems in the case when novel topics turn up, that the
user does not know of in advance and cannot declare an interest in. A
human editor can in this situation act pro-actively, and redistribute
this information based on his or her own judgement.
- Added value:
- The information broker uses his or her professional competence to
add value to the information search: he or she can restructure,
comment, illustrate or rewrite the information to fit the targeted
user population.
5. References
- C. Ahlberg, B. Shneiderman (1994)
- Visual Information Seeking:
Tight Coupling of Dynamic Query Filters with Starfield Displays, in
Proceedings CHI'94: Human Factors in Computing Systems, pages 313 -317.
- Bonsall, P. W., and Joint, M. (1991)
- Evidence on Drivers' Reaction to In-Vehicle Route Guidance
Advice, 24th ISATA International Symposium on Automotive
Technology and Automation, Florence, Italy, 1991.
Boyle, Craig, and Encarnacio, Antonio O. (1994)
- Metadoc: An Adaptive Hypertext Reading System, User Models
and User-Adaptive Interaction, UMUAI 4, pp. 1 - 19.
- Brusilovsky, P. (1996)
- Methods and Techniques of Adaptive
Hypermedia, Journal of User Modeling and User-Adaptive
Interaction, UMUAI 6.
- Cook, R. and Kay, J. (1994)
- The Justified User Model: A Viewable, Explained User Model,
Proc. of the Fourth International Conference on User Modeling,
Hyannis, Mass., The Mitre Corp.
- Höök, K. (1996)
- A Glass Box Approach to Adaptive
Hypermedia, Ph.D. Thesis, SICS Dissertation Series 23, ISBN:
91-7153-510-1, Stockholm, Sweden.
- Höök, K., Karlgren, J., Waern, A., Dahlbäck, N.,
Jansson, C-G., Karlgren, K. and Lemaire, B. (1996)
- A Glass Box
Approach to Adaptive Hypermedia, Journal of User Modeling and
User-Adaptive Interaction, UMUAI 6.
- Maes, P. (1994)
- Agents
that Reduce Work and Information Overload,
Communications of the ACM, Vol.
37, No.7,pp. 31-40, 146, ACM Press, July 1994.
- Maes, P. (1995)
- Intelligent
Software: Programs that can act independently will ease the burdens
that computers put on people, Scientific American, Vol. 273,
No. 3, pp. 84-86, Scientific American, Inc., September 1995.
- Shneiderman, B. (1987)
- Designing the User Interface: Strategies
for Effective Human Computer Interaction, Addison-Wesley.