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Real-Time Ideation Analyzer and Information Recommender

  • The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.

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Metadaten
Author:Midhad BlazevicORCiD, Lennart B. SinaORCiD, Cristian A. SeccoORCiD, Melanie SiegelORCiD, Kawa NazemiORCiD
URN:urn:nbn:de:hebis:ds114-opus4-4422
DOI:https://doi.org/10.3390/electronics13091761
ISSN:2079-9292
Parent Title (English):Electronics
Publisher:MDPI
Place of publication:Basel
Editor:George Angelos Papadopoulos
Document Type:Article
Language:English
Year of Completion:2024
Publishing Institution:Hochschule Darmstadt
Release Date:2024/06/17
Tag:ideation support; large language models; natural language processing; real-time recommendation systems; transdisciplinary ideation
GND Keyword:-
Volume:13
Issue:9
Article Number:1761
Note:
Originalveröffentlichung: Blazevic, M.; Sina, L.B.; Secco, C.A.; Siegel, M.; Nazemi, K. Real-Time Ideation Analyzer and Information Recommender. Electronics 2024, 13, 1761. https://doi.org/10.3390/electronics13091761
Institutes:Media
OpenAIRE:OpenAIRE
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International