Cross-domain recommendation (CDR) aims to alleviate data sparsity by transferring knowledge across domains, yet existing methods primarily rely on coarse-grained behavioral signals and often overlook intra-domain heterogeneity in user preferences. We propose Multi-TAP, a multi-criteria target-adaptive persona framework that explicitly captures such heterogeneity through semantic persona modeling. To enable effective transfer, Multi-TAP selectively incorporates source-domain signals conditioned on the target domain, preserving relevance during knowledge transfer. Experiments on real-world datasets demonstrate that Multi-TAP consistently outperforms state-of-the-art CDR methods, highlighting the importance of modeling intra-domain heterogeneity for robust cross-domain recommendation. The codebase of Multi-TAP is currently available at https://github.com/archivehee/Multi-TAP.
Autistic children often face challenges in social interaction and communication, impacting their social connectivity, especially with their parents. Despite the effectiveness of game-based interactive therapy in improving motor skills, research on enhancing parent-child relationships is lacking. We address this gap with Magicarpet, an interactive play carpet that encourages parent-child interaction and has been validated through a user study with five families. The preliminary results indicate that Magicarpet enhances the motivation and participation of autistic children in play, demonstrating the potential of human-computer interaction (HCI) designs to foster connectivity.
With the introduction of the Visualization for Communication workshop (VisComm) at IEEE VIS and in light of the COVID-19 pandemic, there has been renewed interest in studying visualization as a medium of communication. However the characteristics and definition of this line of study tend to vary from paper to paper and person to person. In this work, we examine the 37 papers accepted to VisComm from 2018 through 2022. Using grounded theory we identify nuances in how VisComm defines visualization, common themes in the work in this area, and a noticeable gap in DEI practices.
This forward-looking paper uses speculative design fiction to explore future museum scenarios where citizen curators design and share immersive virtual reality museums populated with tangible heritage artefacts, intangible virtual elements and interactive experiences. The work also explores takeaway 'asset packs' containing 3D artefact models, curation assets, and interactive experiences, and we envisage a visit to the future museum, where the physical and virtual experiences interplay. Finally, the paper considers the implications of this future museum in terms of resources and the potential impacts on traditional museums.
AI-generated content is rapidly becoming a salient component of online information ecosystems, yet its influence on public trust and epistemic judgments remains poorly understood. We present a large-scale mixed-design experiment (N = 1,000) investigating how AI-generated credibility scores affect user perception of political news. Our results reveal that AI feedback significantly moderates partisan bias and institutional distrust, surpassing traditional engagement signals such as likes and shares. These findings demonstrate the persuasive power of generative AI and suggest a need for design strategies that balance epistemic influence with user autonomy.
This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt the MaxDiff method to reduce the number of survey responses in half, making it less tedious from the survey takers' perspective. We present a case study using the adapted MaxDiff method for tablet feature prioritization research involving users with disabilities.
Neuroaesthetics is an interdisciplinary field that brings together neuroscience, psychology, and the arts to explore how the human brain perceives and responds to visual beauty. This paper examines the neural mechanisms behind aesthetic experiences, aiming to explain why certain designs or artworks feel emotionally or cognitively "right." By analyzing the interaction between perception, emotion, and cognition, neuroaesthetics reveals how beauty is constructed in the brain and how this understanding can inform fields such as graphic and interface design. This paper offers a clear and accessible overview of core neuroaesthetic principles, making the subject approachable to a wide audience. The findings suggest that impactful design is more than surface-level appeal: well-crafted visual experiences can engage, support, and connect people in meaningful ways.
Human digital twins (HDTs) are dynamic, data-driven virtual representations of individuals, continuously updated with multimodal data to simulate, monitor, and predict health trajectories. By integrating clinical, physiological, behavioral, and environmental inputs, HDTs enable personalized diagnostics, treatment planning, and anomaly detection. This paper reviews current approaches to HDT modeling, with a focus on statistical and machine learning techniques, including recent advances in anomaly detection and failure prediction. It also discusses data integration, computational methods, and ethical, technological, and regulatory challenges in deploying HDTs for precision healthcare.
The direct purpose of this paper - as its title suggests - is to present how the visual evaluator extension is implemented in the GRASP programming system. The indirect purpose is to provide a tutorial around the design of GRASP, and in particular - around the architecture of its extension mechanism. Neither GRASP nor its extension mechanisms are, at the moment of writing this paper, final or complete, and we are certain that some details of the solutions described in here will change even before the first release. What will not change, though, is the set of problems that need to be solved in order to build a system with capabilities similar to those of GRASP. We believe that these problems might be of interest to the Scheme community.
This study delves into the interplay between architectural spaces and human emotions, leveraging the emergent field of neuroarchitecture. It examines the functional and aesthetic influence of architectural design on individual users, with a focus on biosensing data such as brainwave and eye tracking information to understand user preferences.
In this paper we consider some empirical materials from our ongoing research into forms of everyday detection and diagnosis work in healthcare settings, and how these relate to issues of trust; trust in people, in technology, processes and in data.
A balanced investigation into the reliability of wireless smart health devices when it comes to the collection of biometric data under varying network/environmental conditions. Followed by a program implementation to begin introductory analysis on measurement accuracy and data collection to gauge the reliability of smart health devices.
Live streaming has become increasingly popular. Our study focuses on the emerging Chinese female audiences who send virtual gifts to young male streamers. We observe a reversed entertainer-viewer gender relationship. We aim to study why they watch young male streamers, why they send gifts, and their relationships with these streamers.
Mixed Reality (MR) devices require a world with always-on sensors and real-time processing applied to their outputs. We have grappled with some of the ethical concerns presented by this scenario, such as bystander privacy issues with smartphones and cameras. However, MR technologies demand that we define and defend privacy in this new paradigm. This paper focuses on the challenges presented by eye tracking and gaze tracking, techniques that have commonly been deployed in the HCI community for years but are now being integrated into MR devices by default.
This is a report on the PLDI 2020 conference, for which I was General Chair, which was held virtually for the first time as a result of the COVID-19 pandemic. The report contains: my personal reflections on the positive and negative aspects of the event; a description of the format of the event and associated logistical details; and data (with some analysis) on attendees' views on the conference format, the extent to which attendees engaged with the conference, attendees' views on virtual vs. physical conferences (with a focus on PLDI specifically) and the diversity of conference registrants. I hope that the report will be a useful resource for organizers of upcoming virtual conferences, and generally interesting to the Programming Languages community and beyond.
This position paper describes an experiment conducted to understand the relationships between different physiological measures including pupil Diameter, Blinking Rate, Heart Rate, and Heart Rate Variability in order to develop an estimation of users' mental load in real-time (see Sidebar 1). Our experiment involved performing a task to spot a correct or an incorrect word or sentence with different difficulties in order to induce mental load. We briefly present the analysis of task performance and response time for the items of the experiment task.
We describe a method of estimating a user's time-on-task in an online learning environment. The method is agnostic of the details of the user's mental activity and does not rely on any data except timestamps of user's interactions, accounting for individual user differences. The method is implemented in R (the code is open-source) and has been tested in the data from a large sample of HarvardX MOOCs.