Regulating recommender systems? Effects of data-based individualization (and its limits) on competition in the digital world - Oliver Budzinski; Annika Stöhr (Technische Universität Ilmenau, Institut für Volkswirtschaftslehre, 2025)
Algorithm- and data-based recommendation systems (DARS) have become a central component of the digital economy, shaping how users access, evaluate, and consume information and goods. These systems encompass both search rankings tailored to estimated user preferences and direct recommendations such as "watch next" or "users also bought". Their growing influence has prompted regulatory interest worldwide, with debates centering on their economic, social, and cultural implications. Drawing on attention economics and behavioral insights, the paper highlights the functional necessity of pre-selection mechanisms in information-overload environments. Personalized DARS improve preference matching, expand the diversity of content receiving attention, and tend to intensify competition - particularly in comparison to one-size-fits-all or editorially curated systems. However, DARS also carry significant risks: they may reinforce biases through self-preferencing, amplify echo chambers, limit exposure to diverse viewpoints, and raise privacy concerns due to their reliance on granular behavioral data. Based on these challenges, this paper provides a comparative institutional analysis of regulatory options for DARS, evaluated through a modern, economicsbased framework. It examines regulatory effects across three key dimensions: (i) preference fit, (ii) information transparency, and (iii) competition intensity. The paper evaluates a range of regulatory strategies, such as transparency obligations, interoperability obligations, randomized rankings, editorial curation, and structural interventions. While each option addresses specific risks, the analysis shows that more interventionist regimes often come at the cost of reduced competition and diminished content diversity. The paper concludes that effective regulation should avoid substituting personalized DARS altogether and instead focus on addressing core pitfalls - particularly those arising from vertical integration and opacity - without eroding the systems' welfare-enhancing functions.
Keywords: B52; D02; D80; K20; L51; L81; L82; L86; 330; recommender systems; attention economics; institutional economics; regulation; competition; algorithms; data; digital economy; privacy