Visual Embedding of Screen Sequences for User-Flow Search in Example-driven Communication
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910865129734144 |
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| author | Jeong, Daeheon Chu, Hyehyun |
| author_facet | Jeong, Daeheon Chu, Hyehyun |
| contents | Effective communication of UX considerations to stakeholders (e.g., designers and developers) is a critical challenge for UX practitioners. To explore this problem, we interviewed four UX practitioners about their communication challenges and strategies. Our study identifies that providing an example user flow-a screen sequence representing a semantic task-as evidence reinforces communication, yet finding relevant examples remains challenging. To address this, we propose a method to systematically retrieve user flows using semantic embedding. Specifically, we design a model that learns to associate screens' visual features with user flow descriptions through contrastive learning. A survey confirms that our approach retrieves user flows better aligned with human perceptions of relevance. We analyze the results and discuss implications for the computational representation of user flows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual Embedding of Screen Sequences for User-Flow Search in Example-driven Communication Jeong, Daeheon Chu, Hyehyun Human-Computer Interaction Effective communication of UX considerations to stakeholders (e.g., designers and developers) is a critical challenge for UX practitioners. To explore this problem, we interviewed four UX practitioners about their communication challenges and strategies. Our study identifies that providing an example user flow-a screen sequence representing a semantic task-as evidence reinforces communication, yet finding relevant examples remains challenging. To address this, we propose a method to systematically retrieve user flows using semantic embedding. Specifically, we design a model that learns to associate screens' visual features with user flow descriptions through contrastive learning. A survey confirms that our approach retrieves user flows better aligned with human perceptions of relevance. We analyze the results and discuss implications for the computational representation of user flows. |
| title | Visual Embedding of Screen Sequences for User-Flow Search in Example-driven Communication |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.06067 |