Searching for analogical ideas with crowds

Yu, Lixiu; Kittur, Aniket; Kraut, Robert E.

doi:10.1145/2556288.2557378

Cited by 34 publications

(15 citation statements)

References 29 publications

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“…For example, a designer who wanted to invent a device to fold laundry for students with very limited fine motor skills might abstract the core function of "folding" to "change surface", which could lead to analogous inspirations like "reefing" (rolling up a portion of a sail in order to reduce its area). Yu et al [27] explored how to systematically train crowd workers to convert problem descriptions into an abstracted form that ignored irrelevant surface details.…”

Section: Abstraction During Analogy-findingmentioning

confidence: 99%

Analogy Mining for Specific Design Needs

Gilon

Chan

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Finding analogical inspirations in distant domains is a powerful way of solving problems. However, as the number of inspirations that could be matched and the dimensions on which that matching could occur grow, it becomes challenging for designers to find inspirations relevant to their needs. Furthermore, designers are often interested in exploring specific aspects of a product-for example, one designer might be interested in improving the brewing capability of an outdoor coffee maker, while another might wish to optimize for portability. In this paper we introduce a novel system for targeting analogical search for specific needs. Specifically, we contribute a novel analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches.

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Section: Abstraction During Analogy-findingmentioning

confidence: 99%

Analogy Mining for Specific Design Needs

Gilon

Chan

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Self Cite

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show abstract

“…Therefore, it is possible for crowds to generate useful ideas, especially if they are the end users of the products. Some specific technique in crowdsourcing idea generation show their effectiveness in improving idea creativity, such as deliberately finding source of analogies from other web-sites [35] and decomposing the initial creative task into sub-problems [24].…”

Section: Related Workmentioning

confidence: 99%

Leveraging Crowd for Collecting and Maintaining Educational Resources for Privacy Learning

Wang¹,

Tao²,

Wang³

et al. 2017

dtcse

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Abstract. As the Internet grows in importance, it is vital to develop methods and techniques for educating end-users to improve their awareness of online privacy. Web-based education tools have been proven effective in many domains and have been increasingly adopted by many online professional and educational services. However, the design and development of Web-based education tools for online privacy is still in its early stage. The traditional solutions always involve privacy experts who have sophisticated expertise. However, it is not clear how inspiring and effective these education tools are to general users of varying backgrounds. Furthermore, such involvement can make the tool development costly. In this paper, we discuss our experiences of designing a quiz-based privacy learning tool by leveraging the wisdom of the crowd on Amazon Mechanic Turk for two purposes: (1) designing the quiz questions for the tool; and (2) evaluating and assessing the effectiveness of the tool. Empirical study demonstrates that the crowd can provide high-quality educational materials for privacy learning.

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“…Existing approaches are to either help people parametrically browse and search for examples [19,17] or extract schemas from examples and search for the schema that allows analogical transfer for a new idea [38,36]. Even with such strategies, the users still have to wade through many examples to either find an inspiring idea or to find the right set of ideas to allow schema induction.…”

Section: Introductionmentioning

confidence: 99%

“…Scalable crowd-powered mechanisms for assessing creativity of individual ideas have already been developed [28,32,35,40,37,39]. However, automated or crowd-powered methods for assessing semantic diversity of sets of ideas are less well developed.…”

Section: Introductionmentioning

confidence: 99%

Toward Collaborative Ideation at Scale

Siangliulue

Arnold

Gajos

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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A growing number of large collaborative idea generation platforms promise that by generating ideas together, people can create better ideas than any would have alone. But how might these platforms best leverage the number and diversity of contributors to help each contributor generate even better ideas? Prior research suggests that seeing particularly creative or diverse ideas from others can inspire you, but few scalable mechanisms exist to assess diversity. We contribute a new scalable crowd-powered method for evaluating the diversity of sets of ideas. The method relies on similarity comparisons (is idea A more similar to B or C?) generated by non-experts to create an abstract spatial idea map. Our validation study reveals that human raters agree with the estimates of dissimilarity derived from our idea map as much or more than they agree with each other. People seeing the diverse sets of examples from our idea map generate more diverse ideas than those seeing randomly selected examples. Our results also corroborate findings from prior research showing that people presented with creative examples generated more creative ideas than those who saw a set of random examples. We see this work as a step toward building more effective online systems for supporting large scale collective ideation.

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Searching for analogical ideas with crowds

Cited by 34 publications

References 29 publications

Analogy Mining for Specific Design Needs

Analogy Mining for Specific Design Needs

Leveraging Crowd for Collecting and Maintaining Educational Resources for Privacy Learning

Toward Collaborative Ideation at Scale

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