Cited by 22Design Education and PracticeData Visualization and AnalyticsCreativity in Education and Neuroscience

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

Open access
Xu, Xiaotong (Tone), Xiong, Rosaleen, Wang, Boyang, Min, David, Dow, Steven P.·2021·Proceedings of the ACM on Human-Computer Interaction

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3479496

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Multiple galleries together help users select a suitable library by comparison.

    “Multiple galleries together help users select a suitable library by comparison [28], [55].”

    From Considering Visualization Example Galleries (2024) · cites this paper as [55]

    The Introduction and Results (sections 1 and 4.2; page numbers unavailable) discuss using one example gallery to find and select relevant examples. A search for multiple galleries and library selection found no passage saying that comparing galleries helps users choose a suitable library. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 16

  1. Salvatore Andolina, Hendrik Schneider, Joel Chan, Khalil Klouche, Giulio Jacucci, and Steven Dow. 2017. Crowdboard: augmenting in-person idea generation with real-time crowds. In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition. 106-118.
  2. Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural language processing with Python: analyzing text with the natural language toolkit. " O'Reilly Media, Inc. ".
  3. Joel Chan, Steven Dang, and Steven P Dow. 2016. IdeaGens: enabling expert facilitation of crowd brainstorming. In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. 13-16.
  4. Joel Chan, Steven P Dow, and Christian D Schunn. 2018. Do the best design ideas (really) come from conceptually distant sources of inspiration? In Engineering a Better Future. Springer, Cham, 111-139.
  5. Minsuk Chang, Léonore V Guillain, Hyeungshik Jung, Vivian M Hare, Juho Kim, and Maneesh Agrawala. 2018. Recipescape: An interactive tool for analyzing cooking instructions at scale. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1-12.
  6. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). · arXiv
  7. Arne Dietrich. 2004. The cognitive neuroscience of creativity. Psychonomic bulletin & review 11, 6 (2004), 1011-1026.
  8. David G Jansson and Steven M Smith. 1991. Design fixation. Design studies 12, 1 (1991), 3-11.
  9. Hyeonsu B Kang, Gabriel Amoako, Neil Sengupta, and Steven P Dow. 2018. Paragon: An online gallery for enhancing design feedback with visual examples. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1-13.
  10. Richard L Marsh, Joshua D Landau, and Jason L Hicks. 1996. How examples may (and may not) constrain creativity. Memory & cognition 24, 5 (1996), 669-680.
  11. Peter Pirolli and Stuart Card. 1999. Information foraging. Psychological review 106, 4 (1999), 643.
  12. Samuel Rhys Cox, Yunlong Wang, Ashraf Abdul, Christian von der Weth, and Brian Y Lim. 2021. Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation. arXiv e-prints (2021), arXiv- 2101.
  13. Daniel Ritchie, Ankita Arvind Kejriwal, and Scott R Klemmer. 2011. d. tour: Style-based exploration of design example galleries. In Proceedings of the 24th annual ACM symposium on User interface software and technology. 165-174.
  14. Pao Siangliulue, Joel Chan, Steven P Dow, and Krzysztof Z Gajos. 2016. IdeaHound: improving large-scale collaborative ideation with crowd-powered real-time semantic modeling. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology. 609-624.
  15. Steven M Smith, Thomas B Ward, and Jay S Schumacher. 1993. Constraining effects of examples in a creative generation task. Memory & cognition 21, 6 (1993), 837-845.
  16. Neil Stewart, Gordon DA Brown, and Nick Chater. 2005. Absolute identification by relative judgment. Psychological review 112, 4 (2005), 881.

Cited by

Sorted by citations

Authors
Xu, X. (., Xiong, R., Wang, B., Min, D., & Dow, S. P.
Affiliations
  • University of California, San Diego, La Jolla, CA, USA
  • The University of Texas at Austin (US)
  • University of California San Diego (US)
Venue
Proceedings of the ACM on Human-Computer Interaction
Published
2021-10-13
Volume
5
Issue
CSCW2
Pages
1-18
ISSN
2573-0142 (ISSN-L)
OpenAlex
W3206850816
Cited by
21
References
50
Citations by year
2026: 1 · 2025: 7 · 2024: 7 · 2023: 1 · 2022: 5
FWCI
1.39
Type
journal article
Language
en
Open access
Locations
Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
Funders
National Science Foundation (1122206,1821618)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
Keywords
artistic inspiration, conceptual distance, semantic similarity, categorical similarity, idea novelty, iterative design, design creativity, adaptive user interfaces

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review