IdeaHound

Abstract

Prior work on creativity support tools demonstrates how a computational semantic model of a solution space can enable interventions that substantially improve the number, quality and diversity of ideas. However, automated semantic modeling often falls short when people contribute short text snippets or sketches. Innovation platforms can employ humans to provide semantic judgments to construct a semantic model, but this relies on external workers completing a large number of tedious micro tasks. This requirement threatens both accuracy (external workers may lack expertise and context to make accurate semantic judgments) and scalability (external workers are costly). In this paper, we introduce IDEAHOUND, an ideation system that seamlessly integrates the task of defining semantic relationships among ideas into the primary task of idea generation. The system combines implicit human actions with machine learning to create a computational semantic model of the emerging solution space. The integrated nature of these judgments allows IDEAHOUND to leverage the expertise and efforts of participants who are already motivated to contribute to idea generation, overcoming the issues of scalability inherent to existing approaches. Our results show that participants were equally willing to use (and just as productive using) IDEAHOUND compared to a conventional platform that did not require organizing ideas. Our integrated crowdsourcing approach also creates a more accurate semantic model than an existing crowdsourced approach (performed by external crowds). We demonstrate how this model enables helpful creative interventions: providing diverse inspirational examples, providing similar ideas for a given idea and providing a visual overview of the solution space.

A Semantic Recommender Engine for Idea Generation Improvement

“…Experimental research confirms that showing users similar ideas and comb ining them to build additional ones can be beneficial and inspirational in the idea generation process (Siangliulue et al, 2016). Thus, collaboration during the ideation process will be helpful in repeatedly generating enhanced qualitative ideas over time.…”

Mapping the Landscape of Creativity Support Tools in HCI

“…(92%, 131 publications), with most intended for use on a laptop or personal computer (52%, 75 publications). These include both web-based and standalone applications, e.g., IdeaHound [159], aimed at supporting creativity through semantic modelling of the spatial structure of a solution space. CSTs intended for miscellaneous or specialized devices that do not fit into our main device categories account for ∼25% of our sample (36 publications).…”

Investigating the Crowd’s Creativity for Creating On-Demand IoT Scenarios

“…For example, we can highlight in the UI untapped IoT categories and devices in the form of visuals similar to a bibliometrics diagram or an Idea Map and encourage crowd-workers to contribute in those categories and devices. This can help to avoid duplication from crowd workers and could lead to a system where a secondary task of feedback (determining the similarity of ideas) can be integrated into the primary task of the scenario generation task (Siangliulue, Chan, Dow, & Gajos, 2016). Nonetheless, this previous study has shown the efficacy of this approach for generating and judging the similarity of short text snippets (e.g., birthday messages).…”