Keyword: recommender systems

Contesting personalized recommender systems: a cross-country analysis of user preferences external link

Starke, C., Metikoš, L., Helberger, N. & Vreese, C.H. de
Information, Communication & Society, 2024

Abstract

Very Large Online Platforms (VLOPs) such as Instagram, TikTok, and YouTube wield substantial influence over digital information flows using sophisticated algorithmic recommender systems (RS). As these systems curate personalized content, concerns have emerged about their propensity to amplify polarizing or inappropriate content, spread misinformation, and infringe on users’ privacy. To address these concerns, the European Union (EU) has recently introduced a new regulatory framework through the Digital Services Act (DSA). These proposed policies are designed to bolster user agency by offering contestability mechanisms against personalized RS. As their effectiveness ultimately requires individual users to take specific actions, this empirical study investigates users’ intention to contest personalized RS. The results of a pre-registered survey across six countries – Brazil, Germany, Japan, South Korea, the UK, and the USA – involving 6,217 respondents yield key insights: (1) Approximately 20% of users would opt out of using personalized RS, (2) the intention for algorithmic contestation is associated with individual characteristics such as users’ attitudes towards and awareness of personalized RS as well as their privacy concerns, (3) German respondents are particularly inclined to contest personalized RS. We conclude that amending Art. 38 of the DSA may contribute to leveraging its effectiveness in fostering accessible user contestation and algorithmic transparency.

Algorithmic contestation, Digital services act, Personalisation, recommender systems

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Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies external link

Senftleben, M., Margoni, T., Antal, D., Bodó, B., van Gompel, S., Handke, C.W., Kretschmer, M., Poort, J., Quintais, J. & Schwemer, S.
JIPITEC, vol. 13, iss. : 1, pp: 67-86, 2022

Abstract

In the European Strategy for Data, the European Commission highlighted the EU’s ambition to acquire a leading role in the data economy. At the same time, the Commission conceded that the EU would have to increase its pools of quality data available for use and re-use. In the creative industries, this need for enhanced data quality and interoperability is particularly strong. Without data improvement, unprecedented opportunities for monetising the wide variety of EU creative and making this content available for new technologies, such as artificial intelligence training systems, will most probably be lost. The problem has a worldwide dimension. While the US have already taken steps to provide an integrated data space for music as of 1 January 2021, the EU is facing major obstacles not only in the field of music but also in other creative industry sectors. Weighing costs and benefits, there can be little doubt that new data improvement initiatives and sufficient investment in a better copyright data infrastructure should play a central role in EU copyright policy. A trade-off between data harmonisation and interoperability on the one hand, and transparency and accountability of content recommender systems on the other, could pave the way for successful new initiatives.

Artificial intelligence, Collective licensing, Content moderation, Copyright, creative industry, cultural diversity, Digital services act, interoperability, market concentration, market failure, metadata, Music Modernization Act, recommender systems, SME, Transparency, trustworthy AI

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Challenging rabbit holes: towards more diversity in news recommendation systems external link

Helberger, N., Bernstein, A., Schulz, W. & Vreese, C.H. de
LSE Media Blog, 2020

Abstract

Access to diverse sources of news and information is more important than ever in this time of global crisis, yet far too often, people can find themselves diving into ‘rabbit holes’ of information and opinion that are hard to escape. Here, the following authors provide an in depth assessment of how algorithmic recommendation systems for news can play a more constructive role in a diverse media landscape.

frontpage, Journalistiek, Mediarecht, nieuws, recommender systems

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Diversity, Fairness, and Data-Driven Personalization in (News) Recommender System external link

Bernstein, A., Vreese, C.H. de, Helberger, N., Schulz, W. & Zweig, K.A.
Dagstuhl Reports, vol. 9, num: 11, pp: 117-124, 2020

Abstract

As people increasingly rely on online media and recommender systems to consume information, engage in debates and form their political opinions, the design goals of online media and news recommenders have wide implications for the political and social processes that take place online and offline. Current recommender systems have been observed to promote personalization and more effective forms of informing, but also to narrow the user’s exposure to diverse content. Concerns about echo-chambers and filter bubbles highlight the importance of design metrics that can successfully strike a balance between accurate recommendations that respond to individual information needs and preferences, while at the same time addressing concerns about missing out important information, context and the broader cultural and political diversity in the news, as well as fairness. A broader, more sophisticated vision of the future of personalized recommenders needs to be formed–a vision that can only be developed as the result of a collaborative effort by different areas of academic research (media studies, computer science, law and legal philosophy, communication science, political philosophy, and democratic theory). The proposed workshop will set first steps to develop such a much needed vision on the role of recommender systems on the democratic role of the media and define the guidelines as well as a manifesto for future research and long-term goals for the emerging topic of fairness, diversity, and personalization in recommender systems.

diversity, fairness, frontpage, Mediarecht, personalisatie, recommender systems

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Designing for the Better by Taking Users into Account: A Qualitative Evaluation of User Control Mechanisms in (News) Recommender Systems external link

Harambam, J., Bountouridis, D., Makhortykh, M. & van Hoboken, J.
RecSys'19: Proceedings of the 13th ACM Conference on Recommender Systems, pp: 69-77, 2019

Abstract

Recommender systems (RS) are on the rise in many domains. While they offer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate different control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they offer, we found that an intelligible user profile (including reading history and flexible preferences settings), coupled with possibilities to influence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users' perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.

diversity, filter bubble, frontpage, Mediarecht, recommender systems, Technologie en recht, Transparency

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My Friends, Editors, Algorithms, and I: Examining audience attitudes to news selection external link

Thurman, N., Möller, J., Helberger, N. & Trilling, D.
Digital Journalism, vol. 2018, 2018

Abstract

Prompted by the ongoing development of content personalization by social networks and mainstream news brands, and recent debates about balancing algorithmic and editorial selection, this study explores what audiences think about news selection mechanisms and why. Analysing data from a 26-country survey (N = 53,314), we report the extent to which audiences believe story selection by editors and story selection by algorithms are good ways to get news online and, using multi-level models, explore the relationships that exist between individuals’ characteristics and those beliefs. The results show that, collectively, audiences believe algorithmic selection guided by a user’s past consumption behaviour is a better way to get news than editorial curation. There are, however, significant variations in these beliefs at the individual level. Age, trust in news, concerns about privacy, mobile news access, paying for news, and six other variables had effects. Our results are partly in line with current general theory on algorithmic appreciation, but diverge in our findings on the relative appreciation of algorithms and experts, and in how the appreciation of algorithms can differ according to the data that drive them. We believe this divergence is partly due to our study’s focus on news, showing algorithmic appreciation has context-specific characteristics.

algoritmes, curation, filtering, frontpage, gatekeeping, Journalistiek, Mediarecht, personalization, recommender systems, user tracking

Bibtex

Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity external link

Möller, J., Trilling, D., Helberger, N. & Es, B. van
Information, Communication & Society, 2018

Abstract

In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the two core concepts in this debate, diversity and algorithms, has received little attention in social scientific research. This paper examines the effect of multiple recommender systems on different diversity dimensions. To this end, it maps different values that diversity can serve, and a respective set of criteria that characterizes a diverse information offer in this particular conception of diversity. We make use of a data set of simulated article recommendations based on actual content of one of the major Dutch broadsheet newspapers and its users (N=21,973 articles, N=500 users). We find that all of the recommendation logics under study proved to lead to a rather diverse set of recommendations that are on par with human editors and that basing recommendations on user histories can substantially increase topic diversity within a recommendation set.

algoritmes, automated content classification, diversity metrics, filter bubbles, frontpage, news, recommender systems

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Exposure diversity as a design principle for recommender systems external link

Helberger, N., Karppinen, K. & D'Acunto, L.
Information, Communication and Society, vol. 2018, num: 2, 2017

Abstract

Personalized recommendations in search engines, social media and also in more traditional media increasingly raise concerns over potentially negative consequences for diversity and the quality of public discourse. The algorithmic filtering and adaption of online content to personal preferences and interests is often associated with a decrease in the diversity of information to which users are exposed. Notwithstanding the question of whether these claims are correct or not, this article discusses whether and how recommendations can also be designed to stimulate more diverse exposure to information and to break potential ‘filter bubbles’ rather than create them. Combining insights from democratic theory, computer science and law, the article makes suggestions for design principles and explores the potential and possible limits of ‘diversity sensitive design’.

autonomy, exposure diversity, filter bubbles, filtering, frontpage, information diversity, medial law, nudging, recommender systems, search enginges, Social media

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