Tri-Collab: A Machine Learning Project to Leverage Innovation Ecosystems in Portugal

This project consists of a digital platform named Tri-Collab, where investors, entrepreneurs, and other agents (mainly talents) can cooperate on their ideas and eventually co-create. It is a digital means for this triad of actors (among other potential ones) to better adjust their requirements. It includes an app that easily communicates with a database of projects, innovation agents and their profiles, and the originality lies in the matching algorithm. Thus, co-creation can have better support through this assertive interconnection of players and their resources. This work also highlights the usefulness of the Canvas Business Model in structuring the idea and its dashboard, allowing a comprehensive view of channels, challenges and gains. Also, the potential of machine learning in improving matchmaking platforms is discussed, especially when technological advancements allow for forecasts and match people at scale.

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Publication Details

Journal
Big Data and Cognitive Computing
Published
2025-05-20
DOI
https://doi.org/10.3390/bdcc9050139
Primary Topic
Service and Product Innovation
Type
article
Field-Weighted Citation Impact
0.00

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article

Tri-Collab: A Machine Learning Project to Leverage Innovation Ecosystems in Portugal

António Marujo, Sílvia Fernandes, Bruno Klaus de Aquino Afonso, Inês de Oliveira Martins et al.
Big Data and Cognitive Computing
Service and Product Innovation
article

Tri-Collab: A Machine Learning Project to Leverage Innovation Ecosystems in Portugal

António Marujo, Sílvia Fernandes, Bruno Klaus de Aquino Afonso, Inês de Oliveira Martins, L.M. Dionísio Pires
article en

Abstract

This project consists of a digital platform named Tri-Collab, where investors, entrepreneurs, and other agents (mainly talents) can cooperate on their ideas and eventually co-create. It is a digital means for this triad of actors (among other potential ones) to better adjust their requirements. It includes an app that easily communicates with a database of projects, innovation agents and their profiles, and the originality lies in the matching algorithm. Thus, co-creation can have better support through this assertive interconnection of players and their resources. This work also highlights the usefulness of the Canvas Business Model in structuring the idea and its dashboard, allowing a comprehensive view of channels, challenges and gains. Also, the potential of machine learning in improving matchmaking platforms is discussed, especially when technological advancements allow for forecasts and match people at scale.

Big Data and Cognitive ComputingVol. 9(5)
University of Algarve (PT)
Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 87%
Service and Product Innovation
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