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Enabled by advances in XR and AI, personalized services are increasingly affecting how individuals perceive, interact with, and navigate their realities. The resulting Personalized Realities (PR) may help people to interact more effectively with their surroundings, and allow more equitable access to information. However, PRs may also disconnect them from a collective understanding through isolated perceptions and perceptual filter bubbles. As democratic societies strive for social cohesion and shared knowledge and experiences, individual PRs may thus further add to existing social fragmentation. Yet, as PRs are framed as a concern that adapts experiences for a single user, they do not capture the full societal implications of a world where personalized mediation of reality is ubiquitous. In this paper, we therefore introduce the term Personalized Society (PSoc) to describe societies in which people predominantly access information and interact with others through a personalized mediation of reality. We discuss the duality of a PSoc, where personalization should be beneficial to the individual but at the same time connect people rather than isolate them. We identify key tensions arising in a PSoc and propose initial design considerations for fostering social cohesion alongside individual PRs, illustrating these through selected scenarios. Finally, we discuss the extent to which regulatory frameworks, such as the Digital Services Act, can be applied to protect individual and societal rights in a PSoc.

Jannis Strecker-Bischoff, Luka Bekavac, Simon Mayer and Kenan Bektaş. 2026. Connecting Personalized Realities: Challenges and Opportunities in a Personalized Society. In Proceedings of 1st Workshop on Shaping Future Human Connection: Social Augmentation through XR Technologies, co-colocated with the ACM CHI 2026 Conference (SAXR ’26). 15 pages

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

@inproceedings{10.1145/3742413.3789129,
author = {Dojcinovic, Sandra and Strecker-Bischoff, Jannis and Mayer, Simon and Bekta\c{s}, Kenan},
title = {Personalized Recommendations in Mixed Reality Enhance Explanation Satisfaction and Hedonic User Experience in Board Game Learning},
year = {2026},
isbn = {9798400719844},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3742413.3789129},
doi = {10.1145/3742413.3789129},
abstract = {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.},
booktitle = {Proceedings of the 31st International Conference on Intelligent User Interfaces},
pages = {1263–1282},
numpages = {20},
keywords = {personalized learning, immersive learning, decision-support systems, board games},
location = {
},
series = {IUI '26}
}

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

@article{10.1145/3795516,
author = {Bekavac, Luka and Strecker-Bischoff, Jannis and Garcia, Kimberly and Mayer, Simon and Tam\`{o}-Larrieux, Aurelia},
title = {Scrutinizing Systemic Risks in Personalized Recommender Systems Through  Sock-Puppet Auditing of VLOPs},
year = {2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3795516},
doi = {10.1145/3795516},
abstract = {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.},
note = {Just Accepted},
journal = {ACM Trans. Recomm. Syst.},
month = jan,
keywords = {Platform Regulation, Social Media, Black-Box testing, Systemic Risks, Filter Bubbles, DSA, Sock-Puppet Auditing}
}