Novelty and diversity: Remixing with Human-based Search Algorithms

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1 Scopus citations

Abstract

Remixing is used as a method to harness collective intelligence in many online innovation communities. In these remixing communities, users can reuse previous work to produce innovative outcomes. However, some users tend to make remix decisions based on what is immediately visible to them. Because of this they miss opportunities to further explore the design space. Can crowd members be guided to conduct collective exploration in a systematic way to better cover the design space? In this paper an exploratory study compares the collective exploration of ideas generated by four human-based remixing algorithms: deepening, widening, depth-first, and breadth-first. The distances between ideas that are generated by these remixing algorithms are calculated and compared. The results suggest that the deepening and hybrid algorithms can encourage users to better cover the design space. Potential ways to further improve the algorithms are discussed, with the goal of encouraging collective exploration through remixing.

Original languageEnglish
Title of host publicationInternational Conference on Information Systems 2018, ICIS 2018
ISBN (Electronic)9780996683173
StatePublished - 2018
Event39th International Conference on Information Systems, ICIS 2018 - San Francisco, United States
Duration: 13 Dec 201816 Dec 2018

Publication series

NameInternational Conference on Information Systems 2018, ICIS 2018

Conference

Conference39th International Conference on Information Systems, ICIS 2018
Country/TerritoryUnited States
CitySan Francisco
Period13/12/1816/12/18

Keywords

  • Collective intelligence
  • Crowdsourcing
  • Digital innovation
  • Human computation
  • Innovation search
  • Remixing

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