Hello all! Today I am going to present our paper
titled Exploring Tangible Algorithmic
Imaginaries in Movie Recommendations. My name
is Oscar Alvarado, and this talk is also on behalf
of Vero Vanden Abeele, David Geerts,
Francisco Gutierrez, and Katrien Verbert.
We all research at
KU Leuven University in Belgium.
Recommender algorithms drive many of our
daily activities and decisions.
They filter the music we listen to,
the restaurants we visit,
and the news we consume.
Unfortunately, research has found
that these systems tend to produce negative
experiences for their users, including lack
of transparency, control, awareness,
and an extensive list of ethical issues
associated with these systems.
Consequently, scholars have also developed
an extensive corpus of solutions.
Nevertheless, to the best of our knowledge,
these solutions have been limited to
traditional Graphical User Interfaces.
Since previous academics have highlighted
how the material instantiation of algorithms
critically shapes their effects
and our experiences with them,
we consider that an opportunity opens for
for tangible user interfaces and create new
experiences with these systems.
But before starting to design tangible
alternatives, it may be necessary to explore
whether users possess tangible
representations of their interactions with
algorithms that could inform
an initial design space.
This kind of exploration requires a
phenomenological strategy to include user’s
different experiences, affects,
and perceptions with algorithmic systems.
we consider a more useful conceptualization
for this purpose what Bucher describes as
algorithmic imaginaries: “the way in which
people imagine, perceive and experience
algorithms, and what these
imaginations make possible”.
Therefore, defining our scope for algorithmic
movie recommendations in Netflix, we decided to
explore: (1) What are algorithmic imaginaries
that users hold of movie recommender systems?
and (2) In what ways designs inspired by tangible
representations of those algorithmic
imaginaries influence a better experience
with recommender algorithms through
tangible user interfaces?
We decided to achieve these goals following
a two-fold strategy. First, we organized co-design
workshops to explore how users produce
tangible representations of
algorithmic imaginaries. Second, we conducted
follow-up interviews to evaluate all these
previous designs and collect
further impressions.
We recruited fifteen active Netflix users via
word-of-mouth, mailing lists, Facebook groups, and
WhatsApp groups related to our university
to apply these methods.
All participants expressed using Netflix
at least once every month, were between
24 and 40 years old, and six of them had a technical
background in computer sciences or similar fields.
Due to their agenda availability, only ten of
these participants were able to participate
in the interviews.
Our paper describes in more detail our participants.
We held five different co-design workshops to
actively engage our participants in
materializing their imaginaries and
experiences with the movie recommendation system.
Each workshop lasted 60 minutes and
was divided into two main parts.
First, a sensitizing activity that ensured and
leveled our participants’ awareness about the
the recommendation system in Netflix.
Second, our participants performed a design task.
They received various materials to creatively
materialize an answer to the question:
“How does Netflix see you?”.
We agreed on a design constraint with the
participants: every answer had to be a tangible creation.
In the end, all participants explained
their creations orally. When needed, , open
questions clarified design intentions.
We also conducted ten individual follow-up
interviews between one to three weeks
after the workshops. Each one started presenting
all the 15 creations produced during the previous
workshops and describing them to the participants.
Afterward, we asked open questions to gather
impressions of the designs and more profound
insights about algorithmic imaginaries
using all the creations as input.
Each interview lasted 30 minutes.
These five workshop discussions and ten Each interview lasted 30 minutes.
These five workshop discussions and ten
interviews were audio-recorded for further
thematic analysis, giving equal weights
to workshops and interviews.
The thematic analysis produced three main themes
of tangible algorithmic imaginaries of movie
recommendations: First, Metaphoric vs. Datafied,
representations; second, Contextual Factors;
and third, Negative Algorithmic Imaginaries.
Due to time constraints in this presentation,
we will present only the first theme that addresses
two main design strategies applied by our
participants to materialize their
algorithmic imaginaries into tangible designs.
The first design strategy is metaphoric representations.
These representations reflect an algorithmic
imaginary where characters and objects act as metaphors,
representing a complex algorithmic profiling
process through a self-reflection of the
recommendation results.
For example, participant seven created a “clown
with a light-saber”, a figure that combines representative
characteristics of the comedies and sci-fi
movies he usually watches.
Other examples presented objects related to
the movie watching context instead of humanoid forms,
as presented by participant nine in D.9.
Here, you see a “castle TV” because he regularly watches
medieval, knights, or castle related shows.
These imaginaries confirm how users portray
and describe a “calculated personality” due to
the interaction with the recommender algorithm.
This "calculated personality" is often represented
using humanoid forms or significant objects
related to the movie watching context.
Users also support these representations by
iconic props commonly known in movies, such as
light-sabers for sci-fi movies.
The second strategy is called datafied representations.
These representations reflect a granular
data-guided strategy using graphs or structured forms,
ordered layers, and hierarchies to
materialize the system’s data.
Design B.5 shows the profile of participant five
using blocks “piling up on each other”.
He explained that “it starts with red blocks
representing Netflix Original content”,
a second layer adds “outside black content”.
For him, “the combination of both layers brings
a third dark-red colored layer”, showing how the
system mixes the user preferences.
Another example is participant six’s B.6
with two different “two moments”:
diverse blocks representing participant six’s
movie preferences on the right side, and “Netflix’s
structured way to transform her tastes on the left side”.
As participant six explained, the “door in the middle
expresses how Netflix tries to transform
her movie tastes into the left side”.
The “calculated personality” in this group
is represented mostly by abstract forms related
to data structures and the recommender system’s inner work
This representation strategy prefers a more
technical approach, supported by bars, blocks of
colors, layers, graphs, and similar forms
of data representations.
We consider that these results offer design
opportunities and suggestions to envision
tangible user interfaces as an alternative
to interact with algorithmic systems
such as movie recommendations.
Without pretending to delimit the entire
design space, we describe some initial ideas.
First, metaphoric representations could serve
serve as an inspiration to design tangible
tangible user interfaces in various forms.
Their main goal would be to follow how users
portray themselves in algorithmic systems.
For example, metaphoric representations could
inspire shape-changing interfaces that
that transform themselves into the current
state of the user profile.
Designers could also allow users to perform
actions to the interface, such as changing
clothes or colors to alter current movie recommendations
However, we agree with our participants that
designs inspired by metaphoric representations
could only offer a superficial level of
transparency and control over the
algorithmic system, failing to scale.
As the understanding of algorithms grows,
users can find these representations very limited,
creating additional questions about the
recommender system rather than providing
answers because of their inherent over generalization
of such a complicated process.
We believe datafied representations can overcome
this limitation, providing a more granular
understanding and control
of algorithmic recommendations.
In this case, the main goal of design opportunities
inspired by datafied representations is to show
the recommender system’s implementation model
as closely as possible, offering detailed
descriptions of data and allowing users to manage
their profile with enough granularity and precision.
While datafied representations are primarily
imagined for graphical user interfaces, our study
shows how users also envision tangible forms
of these representations, proving that
tangible interfaces are still welcome for this
design strategy, allowing people to “touch” the
data and bringing novel opportunities to “make
sense” of its gathering, processing, and implications.
For instance, designers could propose tangible
interfaces that show how much of each movie genre
the system is currently considering
for the recommendations.
To add control functions, the user could perform
manual actions on the interface to reduce or increase
genres or other categories, allowing users
to alter their recommendations for their current needs.
In our paper, we also present a set of design
suggestions based on our findings to further
develop tangible interfaces in this context.
Due to the time constraints, we cannot dig too much into them,
but please check them in detail in our paper.
Thank you very much for watching.
Part of this research has been supported by
by the KU Leuven Research Council,
and the University of Costa Rica.
Please do not hesitate in contacting us for
further information about this research.
See you soon.