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IdeateRelate: An Examples Gallery That Helps Creators Explore Ideas in Relation to Their Own

Authors:

Xiaotong (Tone) Xu,

Rosaleen Xiong,

Steven P. DowAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2

Article No.: 352, Pages 1 - 18

https://doi.org/10.1145/3479496

Published: 18 October 2021 Publication History

Abstract

Creating truly original ideas requires extensive knowledge of existing ideas. Navigating prior examples can help people to understand what has already been done and to assess the quality of their own ideas through comparison. The creativity literature has suggested that the conceptual distance between a proposed solution and a potential inspiration can influence one's thinking. However, less is known about how creators might use data about conceptual distance when exploring a large repository of ideas. To investigate this, we created a novel tool for exploring examples called IdeateRelate that visualizes 600+ COVID-related ideas, organized by their similarity to a new idea. In an experiment that compared the IdeateRelate visualization to a simple list of examples, we found that users in the Viz condition leveraged both semantic and categorical similarity, curated a more similar set of examples, and adopted more language from examples into their iterated ideas (without negatively affecting the overall novelty). We discuss implications for creating adaptive interfaces that provide creative inspiration in response to designers' ideas throughout an iterative design process.

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Cited By

Xu XLiu XZhang HWang WNepal SSefidgar YSeo WKuehn KHuckins JMorris MNurius PRiskin EPatel SAlthoff TCampbell ADey AMankoff J(2024)GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior ModelingGetMobile: Mobile Computing and Communications10.1145/3686138.368614728:2(23-30)Online publication date: 31-Jul-2024
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IdeateRelate: An Examples Gallery That Helps Creators Explore Ideas in Relation to Their Own
1. Human-centered computing

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CSCW2

CSCW2

October 2021

5376 pages

EISSN:2573-0142

DOI:10.1145/3493286

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 18 October 2021

Published in PACMHCI Volume 5, Issue CSCW2

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https://dl.acm.org/doi/10.1145/3686138.3686147
Meegahapola LHassoune HGatica-Perez D(2024)M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595918:2(1-30)Online publication date: 15-May-2024
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