Jannis Strecker-Bischoff
Last Name
Strecker-Bischoff
First name
Jannis
Email
jannis.strecker-bischoff@unisg.ch
ORCID
Phone
+41 71 224 27 63
35 results
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Item type:Publication, Scrutinizing Systemic Risks in Personalized Recommender Systems Through Sock-Puppet Auditing of VLOPs(ACM, 2026-01-30); ; ; ; 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.Type:journal articleJournal:ACM Transactions on Recommender SystemsDOI:10.1145/3795516 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload(Taylor and Francis (United Kingdom), 2026-06-30); ; Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy’s legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person’s most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.Type:journal articleJournal:Behaviour & Information Technology - Some of the metrics are blocked by yourconsent settings
Item type:Publication, NeighboAR: Efficient Object Retrieval using Proximity-and Gaze-based Object Grouping with an AR SystemHumans only recognize a few items in a scene at once and memorize three to seven items in the short term. Such limitations can be mitigated using cognitive offloading (e.g., sticky notes, digital reminders). We studied whether a gaze-enabled Augmented Reality (AR) system could facilitate cognitive offloading and improve object retrieval performance. To this end, we developed NeighboAR, which detects objects in a user's surroundings and generates a graph that stores object proximity relationships and user's gaze dwell times for each object. In a controlled experiment, we asked N=17 participants to inspect randomly distributed objects and later recall the position of a given target object. Our results show that displaying the target together with the proximity object with the longest user gaze dwell time helps recalling the position of the target. Specifically, NeighboAR significantly reduces the retrieval time by 33%, number of errors by 71%, and perceived workload by 10%.Type:journal articleJournal:Proceedings of the ACM on Human-Computer InteractionVolume:8Issue:ETRADOI:10.1145/3655599Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards new realities: implications of personalized online layers in our daily lives(Oldenbourg Wissenschaftsverlag, 2024-06-18); Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gaze-enabled activity recognition for augmented reality feedback(2024-03-16); ; ; ; Andrew DuchowskiHead-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they provide insights into user attention, intentions, and activities, and allow novel interaction methods based on this information. However, in physical environments, the implications of using gaze-enabled AR for human activity recognition have not been explored in detail. In an experimental study with the Microsoft HoloLens 2, we collected gaze data from 20 users while they performed three activities: Reading a text, Inspecting a device, and Searching for an object. We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved up to 89.6% activity-recognition accuracy. Based on the recognized activity, our system—GEAR—then provides users with relevant AR feedback. Due to the sensitivity of the personal (gaze) data GEAR collects, the system further incorporates a novel solution based on the Solid specification for giving users fine-grained control over the sharing of their data. The provided code and anonymized datasets may be used to reproduce and extend our findings, and as teaching material.Type:journal articleJournal:Computers & GraphicsVolume:119Issue:Special Section on Eye Gaze VISAScopus© Citations 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MR Object Identification and Interaction: Fusing Object Situation Information from Heterogeneous Sources(ACM, 2023-09-28); ; The increasing number of objects in ubiquitous computing environments creates a need for effective object detection and identification mechanisms that permit users to intuitively initiate interactions with these objects. While multiple approaches to such object detection-including through visual object detection, fiducial markers, relative localization, or absolute spatial referencing-are available, each of these suffers from drawbacks that limit their applicability. In this paper, we propose ODIF, an architecture that permits the fusion of object situation information from such heterogeneous sources and that remains vertically and horizontally modular to allow extending and upgrading systems that are constructed accordingly. We furthermore present BLEARVIS, a prototype system that builds on the proposed architecture and integrates computer-vision (CV) based object detection with radio-frequency (RF) angle of arrival (AoA) estimation to identify BLE-tagged objects. In our system, the front camera of a Mixed Reality (MR) head-mounted display (HMD) provides a live image stream to a vision-based object detection module, while an antenna array that is mounted on the HMD collects AoA information from ambient devices. In this way, BLEARVIS is able to differentiate between visually identical objects in the same environment and can provide an MR overlay of information (data and controls) that relates to them. We include experimental evaluations of both, the CV-based object detection and the RF-based AoA estimation, and discuss the applicability of the combined RF and CV pipelines in different ubiquitous computing scenarios. This research can form a starting point to spawn the integration of diverse object detection, identification, and interaction approaches that function across the electromagnetic spectrum, and beyond.Type:journal articleJournal:Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)Volume:7Issue:3DOI:10.1145/3610879Scopus© Citations 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Personalized Recommendations in Mixed Reality Enhance Explanation Satisfaction and Hedonic User Experience in Board Game Learning(Association for Computing Machinery, 2026-03-23); ; 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.Type:conference paperScopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ClearSkies: A Preliminary Study of Gaze-Mapped Scene Segmentation in Training Aircraft Cockpits(2026-06-01); ; In pilot training, deviation from standard procedures is a significant concern. To provide student pilots with objective feedback in postflight debriefing, we captured pilots’ view and gaze with the Pupil Core eye-tracker. Then we conducted a preliminary evaluation to test the feasibility of existing scene segmentation models for gazemapping1. We used an OpenCV baseline model for coarse inside vs. outside-analysis, a fine-tuned Detectron2 model for specific instrument segmentation, and Segment Anything Models (SAM 2 and SAM 3) for human-in-the-loop analysis. The baseline was fast but fragile, failing in common flight scenarios; the Detectron2 model was powerful but inflexible and unsuitable for general use; and SAM 3 was promising, offering generalizability for post-flight analysis despite noisy digital displays. A qualitative preliminary evaluation of SAM with Visual Flight Rules shows that it can be beneficial in eye movement analysis. We identified poor data quality in bright cockpit environments and ergonomics as main limitations.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Societally Beneficial Personalized Realities: A Conceptual Foundation for Responsible Ubiquitous Personalization SystemsPersonalization of online realities is today ubiquitous to support decision making or reduce information overload. Recently, through the expanding capabilities and pervasiveness of Mixed Reality and Ubiquitous Computing technologies, we observe increasing personalization also of physical reality. This might yield more convenient, efficient and inclusive everyday interactions. However, it may readily lead to serious societal consequences such as the loss of shared worlds and the emergence of perceptual filter bubbles. To mitigate such harms while retaining the benefits of personalization, it is important to understand how ubiquitous personalization systems may operate responsibly. Responding to this need, we propose a conceptual model that overcomes the limitations of established personalization models and expands their applicable scope to physical, virtual, and hybrid environments. We validated our model in relation to existing literature and show how it provides a conceptual foundation for the analysis and study of responsible personalization systems that create individually and societally beneficial Personalized Realities.Type:conference paperScopus© Citations 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ad-Blocked Reality: Evaluating User Perceptions of Content Blocking Concepts Using Extended Reality(Association for Computing Machinery, 2025-04-26); Scopus© Citations 14