Discovering Diverse, High Quality Design Ideas From a Large Corpus

This paper describes how to select diverse, high quality, representative ideas when the number of ideas grow beyond what a person can easily organize. When designers have a large number of ideas, it becomes prohibitively difficult for them to explore the scope of those ideas and find inspiration. We propose a computational method to recommend a diverse set of representative and high quality design ideas and demonstrate the results for design challenges on OpenIDEO — a web-based online design community. Diversity of these ideas is defined using topic modeling to identify latent concepts within the text while the quality is measured from user feedback. Multi-objective optimization then trades off quality and diversity of ideas. The results show that our approach attains a diverse set of high quality ideas and that the proposed method is applicable to multiple domains.Copyright © 2016 by ASME

[1]  Eric von Hippel,et al.  Democratizing innovation: The evolving phenomenon of user innovation , 2005 .

[2]  Laura Langohr,et al.  Methods for Finding Interesting Nodes in Weighted Graphs , 2014 .

[3]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[4]  John D. Lafferty,et al.  Beyond independent relevance: methods and evaluation metrics for subtopic retrieval , 2003, SIGIR.

[5]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  David Wallace,et al.  Assessing the Quality of Ideas From Prolific, Early-Stage Product Ideation , 2010 .

[8]  Xiaojin Zhu,et al.  Improving Diversity in Ranking using Absorbing Random Walks , 2007, NAACL.

[9]  Carolyn Conner Seepersad,et al.  Crowd-sourcing the evaluation of creativity in conceptual design: A pilot study , 2014 .

[10]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[11]  Timothy M. Kowalewski,et al.  Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations , 2015 .

[12]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[13]  David M. Blei,et al.  Relational Topic Models for Document Networks , 2009, AISTATS.

[14]  Hui Lin,et al.  A Class of Submodular Functions for Document Summarization , 2011, ACL.

[15]  D. Mimno,et al.  Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements , 2014 .

[16]  Md. Monirul Islam,et al.  A review on automatic image annotation techniques , 2012, Pattern Recognit..

[17]  Jade Goldstein-Stewart,et al.  The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries , 1998, SIGIR Forum.

[18]  Alice M. Agogino,et al.  Automatically Inferring Metrics for Design Creativity , 2013 .

[19]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[20]  Jami J. Shah,et al.  Evaluation of idea generation methods for conceptual design: Effectiveness metrics and design of experiments , 2000 .

[21]  C. Elkan,et al.  Topic Models , 2008 .

[22]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[23]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[24]  U. Berkeley.  Exploring Content Models for Multi-Document Summarization , 2018 .

[25]  David M. Mimno,et al.  Care and Feeding of Topic Models , 2014, Handbook of Mixed Membership Models and Their Applications.

[26]  Barclay Kamb,et al.  Linus Pauling: Selected Scientific Papers , 2002 .

[27]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[28]  L. Shu,et al.  Investigating effects of oppositely related semantic stimuli on design concept creativity , 2012 .

[29]  Ralf Krestel,et al.  Latent dirichlet allocation for tag recommendation , 2009, RecSys '09.

[30]  Alice M. Agogino,et al.  How Online Design Communities Evolve Over Time: The Birth and Growth of OpenIDEO , 2014 .

[31]  Hui Lin,et al.  Learning Mixtures of Submodular Shells with Application to Document Summarization , 2012, UAI.

[32]  Mark Fuge,et al.  Network Analysis of Collaborative Design Networks: A Case Study of OpenIDEO , 2014 .

[33]  W. Bruce Croft,et al.  LDA-based document models for ad-hoc retrieval , 2006, SIGIR.

[34]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.