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Board games often involve strategic decision making and procedural planning tasks. Such tasks require learners to make decisions based on dynamically evolving game state and changing information that is situated in a physical environment. Recommender systems can filter available information and provide learners with personalized and actionable suggestions that simplify their decision making while playing board games. Such recommendations can further be spatially aligned with relevant physical elements through Mixed Reality (MR). We present an MR system called GLAMRec for an engine-building strategy board game. GLAMRec provides personalized, transparent recommendations by integrating user data, real-time game state tracking, and ontology-based reasoning during a complex board game, which we use as a proxy environment for procedural learning tasks. We interviewed six board game designers to improve the GLAMRec and conducted a within-subjects design user study (N=32) to investigate how personalized explanations affect explanation satisfaction, user experience, and trust. We found that personalized recommendations significantly improve explanation satisfaction and hedonic user experience without affecting trust ratings, recommendation compliance, and game performance. These findings suggest that personalization primarily shaped perception of enjoyment rather than measurable learning outcomes or trust.

Sandra Dojcinovic, Jannis Strecker-Bischoff, Simon Mayer, and Kenan Bektaş. 2026. Personalized Recommendations in Mixed Reality Enhance Explanation Satisfaction and Hedonic User Experience in Board Game Learning. In 31st International Conference on Intelligent User Interfaces (IUI ’26), March 23–26, 2026, Paphos, Cyprus. ACM, New York, NY, USA, 20 pages. https://doi.org/10.1145/3742413.3789129

Very Large Online Platforms (VLOPs) use personalized recommender systems to optimize their main performance metric: attention-based user engagement. In doing so, these systems might however amplify systemic risks by promoting controversial or polarizing content, thereby exacerbating issues such as misinformation, societal polarization, and the manipulation of civic discourse. To mitigate these risks, regulations such as the European Union’s Digital Services Act (DSA) mandate increased data access and transparency, including for the auditing of personalized recommender systems. However, the data access provided by VLOPs remains limited—often restricted to specific user demographics, aggregate statistics, or curated datasets—hindering meaningful oversight. Consequently, new methods are needed to audit recommender systems effectively at the user level. In this paper, based on an analysis of the legal context and technical alternatives for data access, we present SOAP, the System for Observing and Analyzing Posts. SOAP is an open-source framework for auditing recommender systems using sock-puppet accounts. It enables fine-grained user-level analysis beyond the constrained data access typically provided by platforms. We detail SOAP’s technical implementation and evaluate its ability to scrutinize systemic risks. Additionally, we tested SOAP in a workshop with over 100 participants and observed a measurable increase in participants’ algorithmic literacy. This demonstrates SOAP’s potential not only for research and regulatory auditing, but also as an educational framework to foster public awareness of algorithmic influence.

Luka Bekavac, Jannis Strecker-Bischoff, Kimberly Garcia, Simon Mayer, and Aurelia Tamò-Larrieux. 2026. Scrutinizing Systemic Risks in Personalized Recommender Systems Through Sock-Puppet Auditing of VLOPs. ACM Trans. Recomm. Syst. Just Accepted (January 2026). https://doi.org/10.1145/3795516

Reading assistants provide users with additional information through pop-ups or other interactive events which might interrupt the fow of reading. We propose that unnoticeable changes can be made in a given text during blinks while the vision is obscured for a short period of time. Reading assistants could make use of such change blindness to adapt text in real time and without infringing on the reading experience. We developed a system to study blink-induced change blindness. In two preliminary experiments, we asked five participants to read six short texts each. Once per text and during a blink, our system changed a predetermined part of each text. In each trial, the intensity and distance of the change were systematically varied. Our results show that text changes — although obvious to bystanders — were difcult to detect for participants. Concretely, while changes that afected the appearance of large text parts were detected in 80% of the occurrences, no line-contained changes were detected.

Kai Schultz, Kenan Bektaş, Jannis Strecker-Bischof, and Simon Mayer. 2025. Open your Eyes: Blink-induced Change Blindness while Reading. In Companion of the the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp Companion ’25), October 12–16, 2025, Espoo, Finland. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3714394.3754398