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 an analogical search engine for expressing and abstracting specific design needs that returns more distant yet relevant inspirations than alternate approaches.
|Original language||American English|
|Title of host publication||CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems|
|Subtitle of host publication||Engage with CHI|
|Publisher||Association for Computing Machinery|
|ISBN (Electronic)||9781450356206, 9781450356213|
|State||Published - 20 Apr 2018|
|Event||2018 CHI Conference on Human Factors in Computing Systems, CHI 2018 - Montreal, Canada|
Duration: 21 Apr 2018 → 26 Apr 2018
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Conference||2018 CHI Conference on Human Factors in Computing Systems, CHI 2018|
|Period||21/04/18 → 26/04/18|
Bibliographical noteFunding Information:
The authors thank the anonymous reviewers for their helpful feedback, and Amir Shapira for his helpful insights into the design process. Dafna Shahaf is a Harry & Abe Sherman assistant professor. This work was supported by NSF grants CHS-1526665, IIS-1149797, IIS-1217559, Carnegie Mellon's Web2020 initiative, Bosch, Google, ISF grant 1764/15, Alon grant, and the HUJI Cyber Security Research Center in conjunction with the Israel National Cyber Bureau in the Prime Minister's Office.
© 2018 ACM.
- Computational analogy
- Product dimensions
- Text embedding