VLDB 2026 Research / reviewers in the wild / expert
Yang Chen Lin
dblp:305/2501
· DBLP profile ↗
4ranked-venue papers
2as first author
4since 2021 · last 2026
0000-0002-2477-4110ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 4 · 2 first-author · 4 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | AIDED: Augmenting Interior Design with Human Experience Data for Designer-AI Co-DesignabstractInterior design often struggles to capture the subtleties of client experience, leaving gaps between what clients feel and what designers can act upon. We present AIDED, a designer-AI co-design workflow that integrates multimodal client data into generative AI (GAI) design processes. In a within-subjects study with twelve professional designers, we compared four modalities: baseline briefs, gaze heatmaps, questionnaire visualizations, and AI-predicted overlays. Results show that questionnaire data were trusted, creativity-enhancing, and satisfying; gaze heatmaps increased cognitive load; and AI-predicted overlays improved GAI communication but required natural language mediation to establish trust. Interviews confirmed that an authenticity-interpretability trade-off is central to balancing client voices with professional control. Our contributions are: (1) a system that incorporates experiential client signals into GAI design workflows; (2) empirical evidence of how different modalities affect design outcomes; and (3) implications for future AI tools that support human-data interaction in creative practice. Yang Chen Lin, Chen-Ying Chien, Kai-Hsin Hou, Hung-Yu Chen, Po-Chih Kuo |
CHI | 1 |
| 2026 | Fit Matters: Format-Distance Alignment Improves Conversational SearchabstractExisting conversational search systems can synthesize information into responses, but they lack principled ways to adapt response formats to users’ cognitive states. This paper investigates whether aligning format and distance, which involves matching information granularity and media to users’ psychological distance, improves user experience. In a between-subjects experiment (N = 464) on travel planning, we crossed two distance dimensions (temporal/spatial × near/far) with four formats varying in granularity (abstract/concrete) and media (text/image-and-text). The experiment established that format–distance alignment reduced users’ risk perceptions while increasing decision confidence, perceptions of information usefulness, ease of use, enjoyment, and credibility, and adoption intentions. Concrete formats imposed higher cognitive load, but yielded productive effort when matched to near-distance tasks. Images enhanced concrete but not abstract text, suggesting multimedia benefits depend on complementarity. These findings establish format–distance alignment as a distinctive and important design dimension, enabling systems to tailor response formats to users’ psychological distance. Yitian Yang, Yugin Tan, Jung-Tai King, Yang Chen Lin, Yi-Chieh Lee |
CHI | 4 |
| 2025 | Understanding How Psychological Distance Influences User Preferences in Conversational versus Web Search
Yitian Yang, Yugin Tan, Yang Chen Lin, Jung-Tai King, Yi-Chieh Lee |
CHI | 3 |
| 2024 | Representing scents: An evaluation framework of scent-related experiences through associations between grounded and psychophysiological data
Yang Chen Lin, Shang-Lin Yu, An-Yu Zhuang, Chiayun Lee, Yao An Ting, Sheng-Kai Lee, Bo-Jyun Lin, Po-Chih Kuo |
Int. J. Hum. Comput. Stud. | 1 |