topical media & game development
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Rate, Recommend, Regret -- an Expert-based Approach to the Personalization of Guided Tours
Anton Eliëns, Yiwen Wang
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abstract
In this paper we propose an approach to generate personalized
guided tours based on a finite collection of tours obtained
by tracking the navigation of expert users.
Our proposal is based on a variant of decision theory,
that uses a regret function to measure the difference between
a proposed decision and a finite collection of expert decisions,
generalized to a finite sequence of discrete choices.
Personalization may then be seen as a minimization
problem over a weighting scheme, expressing the relative importance
of experts of which tours are available.
We illustrate our approach by showing how we may obtain
guided tours in 3D digital dossiers containing information
on contemporary art installations,
and discuss how our approach may be applied in other
cultural heritage applications.
keywords:
decision theory, personalization,
guided tours, digital dossier, cultural heritage
Introduction
Leaving all responsibility for interaction to the user
is usually not a good choice, in particular when an information system contains
complex, highly interrelated information.
In
[Eliens et al. (2006b)],
[Wang et al. (2006)],
[van Riel et al. (2006)] we describe the
3D digital dossier format,
in which we presented the information of respectively the Dutch-Serbian artist
Marina Abramovic and the Australian
artist
Jeffrey Shaw,
contemporary artists with a variety of work, ranging from video to art installations.
The
digital dossier supports navigation using a concept graph and allows
for presenting media-rich material, including 3D models of artwork installations.
The digital dossiers have been implemented using X3D/
VRML
to allow for deployment on the web.
Recently we have explored guided tours in digital dossiers, [van Riel et al. (2006)],
which actually automate user interaction, by mimicking user actions through events generated
by a script.
Although this provides an easy way to create guided tours, this does not solve the problem
of what to select as elements in the guided tour, or how to personalize
these tours in an intelligent manner.
In this paper, we discuss techniques from decision theory
as a means to aid the construction of guided tours by consulting an advice
function based on tracking the navigation behavior of expert users.
We will also indicate how a similar advice function can be used for
personalizing tours in cooperation with a recommender system for artworks,
by altering the weight given to particular properties.
structure
The structure of this paper is as follows.
In section 2 we will briefly describe the abramovic dossier.
In section 3 we will give a brief introduction to decision theory,
and in sections 4 and 5 we will discuss how techniques from decision theory can be
applied to the construction of guided tours in digital dossiers by using expert advice.
In section 6, we will illustrate how to apply decision theory for the personalization of
tours in a more conventional cultural heritage application and in section 7 we
will discuss how to realize expert advice functions in digital dossiers.
Finally, in section 8 we will
give our conclusions and indicate directions for future research.
The abramovic dossier
As a user interface for navigating the abramovic dossier,
we created a concept graph, fig. 1(a), that represents arbitrary information structures
in a hierarchical way.
The concept graph allows the user to detect relations and search for information.
Unlike the 3D cone tree, [Robertson and MacKinlay (1991)], where the complete hierarchical structure is presented,
only a subset of the hierarchy is shown - three levels deep.
|
|
|
| Fig. 1(a) Concept Graph | (b) Presentation gadget |
Presentation is an essential part of the digital dossier but is separated from navigation.
The digital dossier contains different presentation facilities for 2D and 3D content.
For 2D media content we need to be able to present video, images or textual information.
This is implemented as a presentation gadget with three windows, fig 1(b).
In each of the three windows the user can view either text, image or video content.
The windows are positioned in such a way that the user can inspect the information simultaneously.
In our experience, three views can be presented at the same time without much visual distortion.
usage scenario:
When starting the dossier, it loads the concept graph that is used to navigate through
the available information.
In the center of the concept graph, a shining star is shown to illustrate the root of the information hierarchy,
which is used as the start object. When clicked, a star structure spreads and child objects appear surrounding
the center star object.
For the purpose of clarity the screenshots have been adapted.
Not only is the originally black background color changed into white,
also the textual information of the nodes has been modified.
Clicking on the Interviews node gives an overview of all interview fragments,
then going back
clicking on the information node Artworks and then on China Ring will bring
the node for China Ring into focus.
When clicking on the center node China Ring, a content presentation environment appears.
which has three windows to present different types of information, grouped into the categories text,
pictures and video.
If desired, the user can focus on any window by using a zoom function.
When the presentation of media content is finished,
clicking on the close button will result in going back to the concept graph.
Alternatively, the home function of the tool bar may be used to return directly to where we started:
the original shining star.
Mathematical preliminaries -- decision theory
Before discussing how to realize guided tours in digital dossiers using user tracking
and expert advice,
we will give a very brief introduction
to decision theory, more in particular a variant of
decsion theory introduced in [Cesa-Bianchi and Lugosi (2006)], that
provides a mathematical foundation for our approach.
In classical prediction theory a prediction is a sequence
of elements that results from a stationary stochastic
process.
The risk of the prediction is taken to be the expected value
of the accumulated loss function, measuring the
discrepancy between predicted values and actual
outcomes.
[Cesa-Bianchi and Lugosi (2006)] introduce a variant of prediction theory in which
no assumption is made with respect to the nature of the
source of predictions.
Instead, the forecaster is considered to
be an entity that gives a prediction for an element
based on advice of one or more experts.
These experts might be actual sequences stored in a database.
The deviation of the forecaster with the actual
outcome is measured using a regret function,
and the prediction task may hence be formulated as
minimimizing the regret function by choosing the
best expert for advice for each element of a prediction sequence.
For example, for the prediction of a bitstring of length n,
the forecaster is a vector of n expert indices,
that give advice for the bitvalue, 0 or 1, in that position.
In the general case, in which we have no information
on the error rate of the experts' advice,
we may use a weighting factor $0 <= %b_{i} <= 1
