Every year design practitioners and researchers develop new methods for understanding users and solving problems. This increasingly large collection of methods causes a problem for novice designers: How does one choose which design methods to use for a given problem? Experienced designers can provide case studies that document which methods they used, but studying these cases to infer appropriate methods for a novel problem is inefficient. This research addresses that issue by applying techniques from content-based and collaborative filtering to automatically recommend design methods, given a particular problem. Specifically, we demonstrate the quality with which different algorithms recommend 39 design methods out of an 800+ case study dataset. We find that knowing which methods occur frequently together allows one to recommend design methods more effectively than just using the text of the problem description itself. Furthermore, we demonstrate that automatically grouping frequently co-occurring methods using spectral clustering replicates human-provided groupings to 92% accuracy. By leveraging existing case studies, recommendation algorithms can help novice designers efficiently navigate the increasing array of design methods, leading to more effective product design.
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October 2014
Research-Article
Machine Learning Algorithms for Recommending Design Methods
Mark Fuge,
Mark Fuge
1
Department of Mechanical Engineering,
Berkeley Institute of Design,
e-mail: mark.fuge@berkeley.edu
Berkeley Institute of Design,
University of California
,Berkeley, CA 94709
e-mail: mark.fuge@berkeley.edu
1Corresponding author.
Search for other works by this author on:
Bud Peters,
Bud Peters
Department of Mathematics,
Berkeley Institute of Design,
e-mail: dbpeters@berkeley.edu
Berkeley Institute of Design,
University of California
,Berkeley, CA 94709
e-mail: dbpeters@berkeley.edu
Search for other works by this author on:
Alice Agogino
Alice Agogino
Department of Mechanical Engineering,
Berkeley Institute of Design,
e-mail: agogino@berkeley.edu
Berkeley Institute of Design,
University of California
,Berkeley, CA 94709
e-mail: agogino@berkeley.edu
Search for other works by this author on:
Mark Fuge
Department of Mechanical Engineering,
Berkeley Institute of Design,
e-mail: mark.fuge@berkeley.edu
Berkeley Institute of Design,
University of California
,Berkeley, CA 94709
e-mail: mark.fuge@berkeley.edu
Bud Peters
Department of Mathematics,
Berkeley Institute of Design,
e-mail: dbpeters@berkeley.edu
Berkeley Institute of Design,
University of California
,Berkeley, CA 94709
e-mail: dbpeters@berkeley.edu
Alice Agogino
Department of Mechanical Engineering,
Berkeley Institute of Design,
e-mail: agogino@berkeley.edu
Berkeley Institute of Design,
University of California
,Berkeley, CA 94709
e-mail: agogino@berkeley.edu
1Corresponding author.
Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 9, 2014; final manuscript received July 21, 2014; published online August 18, 2014. Assoc. Editor: Irem Y. Tumer.
J. Mech. Des. Oct 2014, 136(10): 101103 (8 pages)
Published Online: August 18, 2014
Article history
Received:
January 9, 2014
Revision Received:
July 21, 2014
Citation
Fuge, M., Peters, B., and Agogino, A. (August 18, 2014). "Machine Learning Algorithms for Recommending Design Methods." ASME. J. Mech. Des. October 2014; 136(10): 101103. https://doi.org/10.1115/1.4028102
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