=Paper=
{{Paper
|id=Vol-3908/paper_49
|storemode=property
|title=Enabling Users’ Control on Recommender Systems for Short Videos: a Design Proposal
for the Implementation of the Requirements of the Digital Services Act
|pdfUrl=https://ceur-ws.org/Vol-3908/paper_49.pdf
|volume=Vol-3908
|authors=Matteo Fabbri,Jingyi Jia,Wolfgang Woerndl
|dblpUrl=https://dblp.org/rec/conf/ewaf/FabbriJW24
}}
==Enabling Users’ Control on Recommender Systems for Short Videos: a Design Proposal
for the Implementation of the Requirements of the Digital Services Act==
https://ceur-ws.org/Vol-3908/paper_49.pdf
Enabling Users’ Control on Recommender Systems for Short
Videos: A Design Proposal for the Implementation of the
Requirements of the Digital Services Act⋆
Matteo Fabbri1, Jingyi Jia2 and Wolfgang Woerndl2
1
IMT School for Advanced Studies, Lucca, Italy
2
Technical University of Munich, Munich, Germany
Abstract
In the contemporary information age, online platforms influence users’ behaviour through
recommender systems (RSs), the algorithms that suggest content to them. The Digital Services
Act (DSA) of the European Union is the first supranational regulation that sets specific
transparency and design requirements for RSs with the aim of enhancing users’ autonomy: apart
from explaining the functioning of their RSs in the terms and conditions, online platforms are
now required to provide a functionality that allows users to modify the parameters on which
recommendations rely. However, the standards on how to design DSA-compliant RSs have not
been published yet, leaving the regulatory ground open to conflicting interpretations. In this
work, we introduce an interactive, controllable short video recommendation platform that will be
used to carry out user studies aimed at clarifying how the user control features in RSs can be
designed so that users understand and are willing to intervene on the recommendation process.
Our contribution proposes original algorithmic and interface design features that can be adopted
by the main short-video platforms with minor refinement to comply with the DSA.
Keywords
Recommender systems, Meaningful control, Interface design, Digital Services Act
1. Introduction
In the contemporary information age, the influence of online platforms on people’s behaviour is
widespread: from scrolling through series of short videos on TikTok, to purchasing a product
suggested by the personalized feed on Amazon, individuals’ choices are increasingly informed by
stimuli coming from digital interfaces. This influence is enabled mainly by recommender systems
(RSs), the algorithms that suggest content to users of online platforms. The Digital Services Act (DSA)
of the European Union [1] is the first supranational regulation that sets specific transparency and
design requirements for RSs with the aim of enhancing users’ autonomy: in particular, “Providers of
online platforms that use recommender systems shall set out in their terms and conditions, in plain
and intelligible language, the main parameters used in their recommender systems, as well as any
options for the recipients of the service to modify or influence those main parameters” (DSA, art.
27.1). While explanations must be granted in any case, the “easily accessible functionality” that allows
users to change the parameters of recommendations shall be provided just when “several options are
available [...] for recommender systems that determine the relative order of information presented to
recipients of the service (DSA, art. 27.3).
Such regulatory focus is justified by the concern that the nudging potential of RSs represents a risk,
especially for vulnerable users: indeed, judicial cases involving platforms’ responsibility for displaying
⋆ EWAF’24: European Workshop on Algorithmic Fairness. July 01-03, 2024. Mainz, Germany
matteo.fabbri@sns.it (M. Fabbri)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
recommendations that may lead to political radicalization or endangerment of minors have recently
caught public attention [2]. In fact, adapting the concept of nudging developed by Thaler and Sunstein
[3], RSs can be defined as “digital nudges”, because they “influence which information is easily
accessible to us and thus affect our decision-making processes through the automated selection and
ranking of the presented content” [4]. As a result, digital nudging through RSs “may lead to
undesirable outcomes, such as manipulation, if users’ perception of the recommendation process is
not informed by the knowledge of how it unfolds” [5] and how they can influence it. The black-box
character of RSs can cause users’ inability to verify the quality of a recommendation or to challenge
an inappropriate outcome. Therefore, to be effective, the application of the new legal requirements
for RSs transparency should be supported by the understanding of how their nudging mechanisms
affect users’ choices.
In order to comply with the DSA, very large online platforms (VLOPs) have progressively provided
explanations of their RSs and developed additional user control mechanisms [6], whose effectiveness
depends mainly on the level of users’ active engagement [7, 8], which is, in turn, strongly influenced
by the design of the interface. However, research in this field is at its initial stage, and the existing
control features are not popular with users [9], probably due to poor design [10] and lack of incentives.
To explore the interactive design of meaningful user control in RSs, this paper introduces an interactive,
controllable short video recommendation platform that will be used to carry out user studies aimed at
answering the following research question: how can the user control features in RSs be designed so that
users understand and are willing to intervene on the recommendation process? Short videos have been
chosen as the target domain of recommendations because they are among the most popular and
attention-consuming online content nowadays, especially among young users.
2. Designing for users’ agency and control of recommender systems
When designing our interactive interface, particular consideration was given to ensuring that the
system is transparent and has a low cognitive load. Transparency is a prerequisite for user control:
users need to understand how the RS works and how they are profiled before they can intervene on
the parameters of the recommendations to inform them according to their preferences [5]. This can
be achieved by introducing appropriate explanations [11], as the user experience is optimised when
both controllability and explainability are provided at the same time [12]. Thus, we added implicit
and explicit explanations to let users know how their actions can affect the system. We also attempted
to strike a balance between providing users with control features and limiting their cognitive load:
indeed, an overabundance of control options and complex functions can lead to users’ confusion,
worsen their experience, and even lead to low-quality recommendations [13, 14].
Users’ characteristics were also considered: Knijnenburg et al. [15] have shown that users’ expertise
and digital literacy often influence their understanding of the RS, thereby affecting their mode of
interaction. People who are familiar with social media applications or have prior knowledge in the
field of computer science tend to use personalized RSs more effectively [16, 17, 18]: consequently, it
is essential to design recommendations considering the needs of both experts and inexperienced users.
A promising approach is to differentiate the interface design by offering offer both elementary and
advanced control features but presenting them in distinct interface spaces [15]. The design guidelines
adopted include:
• using common visualizations,
• simplifying the complexity of the interaction,
• implementing dynamic feedback to assist comprehension, and
• presenting control features with different levels of complexity in separate interfaces.
3. Building an interactive recommendation interface: a three-layered
approach
Mainstream short-video platforms were chosen as the primary design references for our platform,
which recommends short videos retrieved from the YouTube API1. The decision of adopting this API
was based on the idea that users would find a familiar interface for short videos more straightforward.
User controls on the interface are categorized into low-, medium-, and high-level, following the three-
layered approach proposed by Jin et al. [20]. Figure 1 shows the interactive components used in this
project and the level of control to which they belong:
• Low-level: Users interact with the recommendations through an evaluative feedback
mechanism. Users can express how much they like the item and why. The RS uses this feedback
to compute the updated user preferences by itself.
• Medium-level: Users can modify their user profile through interactive components, view and
adjust the personalized metrics calculated by the system and make secondary adjustments to
the aggregated preference results.
• High-level: Users directly intervene on the recommendation algorithm. In this project, users
can adjust the relative weights of the two models adopted by a hybrid RS at any time.
To produce recommendations, we adopted a hybrid RS based on a personalized Topic-Based
algorithm and a non-personalized Top-Popular algorithm, which provide candidate recommendations
before a re-ranking algorithm generates the final list of videos. The proposed interface consists of two
main pages: the video page, where short videos are displayed, and the user profile page. Following the
design strategy recommended by Knijnenburg et al. [15], simple and complex interaction components
are presented in separate spaces. The video page primarily features low-level controls, while the user
profile page contains medium and high-level controls. This design assumes that, as the video page
will be seen by all users, it should contain only simple control components for low-level
customization. Expert users can switch to the user profile page for more advanced controls, depending
on their interests.
On the video page (Figure 2), users participate in the recommendation process through low-level
control components: as they watch videos, they can directly express their preferences using like and
dislike buttons, or provide more detailed feedback, such as reasons for disliking a video, using a
supplementary button. The information button allows users to see why a video has been
recommended (explicit explanation) and the topic tags associated with the video, which, if hovered
over, display a word cloud in which users can understand the range of topics underlying the tag
(implicit explanation). Users can also click on either a green or red button under the word cloud, which
correspond, respectively, to a Topic Preference: Like Button Dislike Button feedback: colours are used
to convey immediately the positive or negative feedback on the corresponding topic. As the controls
available on this page are primarily low-level, users do not directly interfere with the recommendation
process, but instead express their preferences and allow the RS to update itself accordingly.
The user profile page (Figure 1) contains medium- and high-level controls. The medium-level
controls allow users to change their topic preferences. We used a pie chart to show the composition
of topic groups in the user profile, with labels indicating the name of the topics within the videos:
here, users can view the scores corresponding to their interest level in the top-ten preferred topics
calculated by the model, and can directly change such scores so that subsequent recommendations
will be based on these user-modified preferences. For the high-level control, users can adjust the
algorithmic parameters to increase or decrease diversity in the personalized Topic-Based algorithm,
as well as the number of recommendations provided by each of the two algorithms.
1
YouTube Data API [19]: https://developers.google.com/youtube/v3
Figure 1: Interactive Control Features in Three Levels Figure 2: Video page
Figure 3: User Profile Page
4. Conclusion and future work
In this work, we presented interactive control features for a recommendation-based interface in the
domain of short videos. Our contribution traces a novel perspective for the application of the
transparency requirements for RSs of the DSA and sets the ground for testing the effectiveness of
users’ empowerment through active and meaningful control, which is a prominent goal of this
European regulation. To continue in this research direction, we are developing and refining the RS
models underlying the interface so that users’ actions are realistically reflected in subsequent video
recommendations. Moreover, our current work focuses on designing a user study to test the
effectiveness of the proposed interaction components. We are planning to analyse users’ behaviour,
including the usage of the interactive control components of the interface and video watch times, and
participants’ explicit feedback collected through post-experiment questionnaires. Such user study,
enabled by the interface presented above, will contribute to understand how meaningful users’ control
can be achieved effectively through the new regulatory incentives.
In conclusion, the design features of this short video recommendation platform can represent a
guideline for the technical standardization of the DSA requirements on RSs, which has not happened
yet. The European Commission has already opened two proceedings under the DSA against a
prominent short-video VLOP like TikTok, the first of which focuses, among other things, on the
"negative effects stemming from the design of TikTok’s system, including algorithmic systems, that
may stimulate behavioural addictions and/ or create so-called ‘rabbit hole effects’" [21]. Therefore,
there is a pressing need for clear design guidelines and standards allowing online platforms to adapt
the control and feedback features of their RSs in compliance with Articles 27 and 38 of the DSA. Our
contribution traces a direction in this regard, by proposing original algorithmic and interface design
features that can be adopted by the main short-video platforms with minor refinement.
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