relative design acquisition a
computational approach for creating
visual interfaces to steer user choices
we mainly motivate our problem from the
following scenario with a shopping
website on the bottom right we have two
buttons that prompt the user to click
either purchase Amazon Prime or continue
without
the two buttons are designed differently
to steer user choices the buy button is
more bright in color and has text with
greater impact whereas the continue
button is less colorful and more faded
we turn the top button as the reference
design because it is optimal in
attracting user attention hence a
reference whereas the bottom button is a
relative design to the top button
motivated by this
scenario the research question we want
to address is can we come up with a
computational method that allows us to
generate these visual relative designs
with respect
to a reference design based on user
feedback
the method we come up with is called
relative design acquisition
which we shorted as RDA
one possible application for RDA is that
in Google Maps currently the most
desired path is shown in blue whereas
the other options are grayed out as
shown
however a secondary preferred option can
be designed with relative design
acquisition to be relative of a shade of
blue or border strike with respect to
the reference most desired path to
prompt the user path taking this could
be used for example in alleviating the
overall traffic in an area
following these examples we
mathematically formalize the problem
into what we call relative design
acquisition acquisition the process is
as follows First Multiple designs are
proposed to users from which we collect
the ratings on the visual interface
we term this as the absolute rating task
as shown on the left next using this
data we can construct the surrogate
model of design performance
we use gaussian processes to construct
this model
we use the model and data then to use to
generate reference designs and given the
reference design and the model we can
generate a relative design of a certain
quality which is parameterized by gamma
if gamma is close to one then that means
the reference and relative designs
should be of similar quality so they
should almost be indistinguishable if
gamma is closer to zero then that means
that the reference design is going to be
of a much higher quality than the
relative design in this case we expect
the user to pick the reference design
over the relative design
we then shown the reference and relative
designs to the user in an A B comparison
test in which we get a comparative
rating of how much the user prefers One
Design over the other
we termed this as the comparative rating
task
we seek to understand the factors
affecting relative design acquisition
for the design of visual interfaces with
three studies
and study 1 we focused on relative
design acquisition for visual
context-free interface design in the
mental context of a shopping website
the following task shows the continuous
slider interface used for the absolute
rating task for the button design task
and
um
as well as for the comparative rating
task
um
they are also similar for the absolute
rating tasks and comparative rating
tasks for the text design task as shown
here
note that this is a continuous slider
and the user can drag
the slider to the extent which he or she
believes that they prefer One Design
over the other
we found in the study that the
comparative ratings can be controllable
by the quality of the relative design
and this approach is able to capture
individual preferences this is true for
both the button design and text tasks as
we can see here there is a difference in
the comparative rating
for different
Gammas or quality factors of the
relative design showing this approaches
efficacy for individualized preferences
and this one shows the distribution of
ratings for the button task
in study 2 we focused on relative design
acquisition for visual interface design
in the visual context of a shopping
website
so the following shows the absolute
rating test and comparative reading task
for the two different website scenarios
namely the blue button rating task and
the green button reading task so it
looks very similar to the setup in study
one but now we have a visual context
we found in the study that the
comparative ratings can be controllable
by the quality of the relative design
um so this method is also effective for
for scenarios where there is a visual
context and the two methods in which we
sample data in order to create the
surrogate model are effective in
generating relative designs for
individualized preferences
in study 3 we focused on relative design
acquisition for a new group of users
by generating relative designs that
could steer user performances for a
group of individuals
so here are all the different types of
relative and reference designs we
generated for the four different types
of tasks for a group of users and we
wanted to see if we could control their
preferences as a population
and in this study we thought that we can
generate relative designs that capture
group preferences
and what is more interesting is that we
can as well capture the quality of the
relative design
in the amount of time it takes for the
participants to finalize their
comparative rating so if gamma is larger
that means that it takes more time for
people to decide which one of the
reference or relative designs they
preferred
overall our method shows promise and
generalizing
to many scenarios where multiple choices
are presented and users are nudged to
make a certain choice
um we can conclude that relative design
acquisition is able to capture
individual preferences in a controllable
way with gamma
um and many sampling methods work well
with relative design acquisition and
that the relative designs generated can
translate to preferences for a new group
of users affecting their decision types
so some applications further
applications for this
could for instance be in a desktop
application where less useful tools are
sub-optimal in their visual appearance
and interface designs with more advanced
features can be adjusted in quality
depending on user proficiency such as in
training wheels for user interface
please read our paper for further
details on the method and experiments