IdeaHound

Siangliulue, Pao; Chan, Joel; Dow, Steven P.; Gajos, Krzysztof Z.

doi:10.1145/2984511.2984578

Cited by 71 publications

(10 citation statements)

References 51 publications

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“…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.…”

Section: Figure 2 Recommendation In Smart Organizationmentioning

confidence: 52%

A Semantic Recommender Engine for Idea Generation Improvement

Haiba¹,

Elbassiti²,

Ajhoun³

2018

CIS

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In order to develop the ability to be competitive in considering rapid ly growing global market and enormously changing in technology, organizations are looking for up -to-date procedures to respond to all these transformations. Being smart and innovative is actually the most significant pillars of successful organization strategies. In other words, organizations need to encourage learning, manage knowledge and create innovative ideas. A major issue of creative ideation is improv ing the quality of the ideas generated. In this paper, we propose a semantic reco mmender engine for idea generation in order to assist organizations to imp rove their ways of generating new ideas. Through this novel system, innovation actors will be ab le to consider new perspectives, make new connections, think differently and thus produce new promising ideas. We initially introduce the concept behind a smart organization, explore the idea generation in such organizations and examine the role of recommender systems for managing this stage and identifying breakthrough ideas. Next, we present the context of design, the conceptual architecture of the suggested system and finally expand the workflo w of semantic similarity matching of ideas with a focus on the key components of the semantic recommendation engine.

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Section: Figure 2 Recommendation In Smart Organizationmentioning

confidence: 52%

A Semantic Recommender Engine for Idea Generation Improvement

Haiba¹,

Elbassiti²,

Ajhoun³

2018

CIS

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“…(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).…”

Section: Devices Supportedmentioning

confidence: 99%

Mapping the Landscape of Creativity Support Tools in HCI

Frich

Vermeulen

Remy

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

206

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Creativity Support Tools (CSTs) play a fundamental role in the study of creativity in Human-Computer Interaction (HCI). Even so, there is no consensus definition of the term 'CST' in HCI, and in most studies, CSTs have been construed as oneoff exploratory prototypes, typically built by the researchers themselves. This makes it difficult to clearly demarcate CST research, but also to compare findings across studies, which impedes advancement in digital creativity as a growing field of research. Based on a literature review of 143 papers from the ACM Digital Library (1999-2018), we contribute a first overview of the key characteristics of CSTs developed by the HCI community. Moreover, we propose a tentative definition of a CST to help strengthen knowledge sharing across CST studies. We end by discussing our study's implications for future HCI research on CSTs and digital creativity. CCS CONCEPTS • Human-centered computing → Interaction design theory, concepts and paradigms; HCI theory, concepts and models; Interactive systems and tools.

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“…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).…”

Section: Artificial Intelligence Guiding Crowd Workers For Creative Imentioning

confidence: 99%

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

Abbas¹,

Markopoulos²,

Khan³

2020

International Journal of Human–Computer Interaction

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The IoT industry supplies a plethora of Internet connected devices and services supporting smart home automation. However, end-users having little knowledge of the features and possibilities of such technologies, face difficulties in conjuring up useful application scenarios combining such devices and services, thus missing out on potential applications outside those provided by vendors. A remedy for such end-users can potentially be found in crowdsourcing IoT scenario creation. For such an enterprise to be viable it is essential to assess whether crowdsourcing can result in practical and original scenarios. This article reports two studies aiming to establish the practicality and originality of crowdsourced IoT scenarios for smart homes. In the first study, we recruited 102 crowd workers who created 306 scenarios in various categories. We then recruited a second cohort of 620 crowd workers to rate the scenarios' creativity. In the second study, we evaluated the corpus of IoT scenarios by 20-experienced smart home users recruited through a screening survey. Our results show that the crowd evaluations of originality and creativity are strongly correlated with those of smart home users. Our major IoT-specific findings in relation to creativity are: a) The number of IoT devices and the number of combination of devices impact how creative the scenarios are perceived; b) Workers with self-reported intermediate programming knowledge wrote more creative scenarios when compared to workers having expert knowledge; c) Computational metrics such as text metrics can provide the basis for automated assessment of the scenarios' creativity. Finally, an inductive thematic analysis of the scenarios revealed interesting themes (e.g., types of rules, automation styles and novel operators) which can serve as a guide for designing more expressive and intuitive end-user development solutions, in the context of IoT.

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IdeaHound

Cited by 71 publications

References 51 publications

A Semantic Recommender Engine for Idea Generation Improvement

A Semantic Recommender Engine for Idea Generation Improvement

Mapping the Landscape of Creativity Support Tools in HCI

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

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