A Recommendation System for Ideation: Enhancing Supermind Ideator

Recommendation systems are widely utilized across various domains such as e-commerce, entertainment, and social media to enhance user experience by personalizing content and suggestions. Despite their widespread use, these systems are rarely applied to the ideation process, presenting unique challen...

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Bibliographic Details
Main Author: Papacica, Daniel
Other Authors: Malone, Thomas W.
Format: Thesis
Published: Massachusetts Institute of Technology 2024
Online Access:https://hdl.handle.net/1721.1/156801
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author Papacica, Daniel
author2 Malone, Thomas W.
author_facet Malone, Thomas W.
Papacica, Daniel
author_sort Papacica, Daniel
collection MIT
description Recommendation systems are widely utilized across various domains such as e-commerce, entertainment, and social media to enhance user experience by personalizing content and suggestions. Despite their widespread use, these systems are rarely applied to the ideation process, presenting unique challenges due to the inherently creative and complex nature of generating and developing novel ideas. This thesis details the creation and assessment of a recommendation system for the Supermind Ideator platform, aimed at enhancing the creative ideation processes. The recommendation system leverages machine learning techniques to dynamically adapt to user input statements based on statement "scope", a sub-task that is thoroughly explored and tested in this paper. "Scope" is then integrated into the recommendation system’s static rules-based algorithm to suggest the next best Supermind Design "move". This work not only contributes a practical tool to the field of ideation but also extends the theoretical understanding of recommendation systems in facilitating complex, subjective cognitive tasks.
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spelling mit-1721.1/1568012024-09-17T03:49:18Z A Recommendation System for Ideation: Enhancing Supermind Ideator Papacica, Daniel Malone, Thomas W. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Recommendation systems are widely utilized across various domains such as e-commerce, entertainment, and social media to enhance user experience by personalizing content and suggestions. Despite their widespread use, these systems are rarely applied to the ideation process, presenting unique challenges due to the inherently creative and complex nature of generating and developing novel ideas. This thesis details the creation and assessment of a recommendation system for the Supermind Ideator platform, aimed at enhancing the creative ideation processes. The recommendation system leverages machine learning techniques to dynamically adapt to user input statements based on statement "scope", a sub-task that is thoroughly explored and tested in this paper. "Scope" is then integrated into the recommendation system’s static rules-based algorithm to suggest the next best Supermind Design "move". This work not only contributes a practical tool to the field of ideation but also extends the theoretical understanding of recommendation systems in facilitating complex, subjective cognitive tasks. M.Eng. 2024-09-16T13:50:06Z 2024-09-16T13:50:06Z 2024-05 2024-07-11T14:37:15.220Z Thesis https://hdl.handle.net/1721.1/156801 Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Copyright retained by author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Massachusetts Institute of Technology
spellingShingle Papacica, Daniel
A Recommendation System for Ideation: Enhancing Supermind Ideator
title A Recommendation System for Ideation: Enhancing Supermind Ideator
title_full A Recommendation System for Ideation: Enhancing Supermind Ideator
title_fullStr A Recommendation System for Ideation: Enhancing Supermind Ideator
title_full_unstemmed A Recommendation System for Ideation: Enhancing Supermind Ideator
title_short A Recommendation System for Ideation: Enhancing Supermind Ideator
title_sort recommendation system for ideation enhancing supermind ideator
url https://hdl.handle.net/1721.1/156801
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