Abstract

User-driven customization is a particular design paradigm where customers act as co-designers to configure products based on their needs. However, due to insufficient product usage experience, customers may design a product incompatible with their environment and needs. Such incompatibility can negatively affect the performance of some customized features or even cause product failure. As a result, customers may hesitate to customize products because additional complexities and uncertainties are perceived. Product usage context (PUC), as all the environment and application factors that affect customer needs and product performance, can be used to facilitate customer co-design in user-driven customization. Identifying individual customer’s PUC can help customers foresee potential design failures, make more holistic design decisions, and be confident with their designs. Against the background, this paper proposes a PUC knowledge graph (PUCKG) construction method using user-generated content (UGC). The proposed method can convert crowdsourced corner cases into structured PUCKG to support personal PUC prediction, summarization, and reasoning. A case study of robot vacuum cleaners is conducted to validate the efficacy of the proposed method.

References

1.
Tseng
,
M. M.
,
Wang
,
Y.
, and
Jiao
,
R. J.
,
2017
, “Mass Customization,”
CIRP Encyclopedia of Production Engineering
, The International Academy for Production,
L
.
Laperrière
, and
G
.
Reinhart
, eds.,
Springer
,
Berlin/Heidelberg
, pp.
1
8
.
2.
Tseng
,
M. M.
, and
Du
,
X.
,
1998
, “
Design by Customers for Mass Customization Products
,”
CIRP Ann.
,
47
(
1
), pp.
103
106
.
3.
Wang
,
Y.
, and
Tseng
,
M. M.
,
2011
, “
Integrating Comprehensive Customer Requirements Into Product Design
,”
CIRP Ann.
,
60
(
1
), pp.
175
178
.
4.
Green
,
M. G.
,
Palani Rajan
,
P. K.
, and
Wood
,
K. L.
,
2004
, “
Product Usage Context: Improving Customer Needs Gathering and Design Target Setting
,”
Proceedings of the ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 3a: 16th International Conference on Design Theory and Methodology
,
Salt Lake City, UT
,
Sept. 28–Oct. 2
, pp.
393
403
.
5.
Ulwick
,
A. W.
, and
Bettencourt
,
L. A.
,
2008
, “
“Giving Customers a Fair Hearing,” (in English)
,”
MIT Sloan Manage. Rev.
,
49
(
3
), pp.
62
68
.
6.
Tseng
,
M. M.
,
Jiao
,
R. J.
, and
Wang
,
C.
,
2010
, “
Design for Mass Personalization
,”
CIRP Ann.
,
59
(
1
), pp.
175
178
.
7.
Schnurr
,
B.
, and
Scholl-Grissemann
,
U.
,
2015
, “
Beauty or Function? How Different Mass Customization Toolkits Affect Customers’ Process Enjoyment
,”
J. Consum. Behav.
,
14
(
5
), pp.
335
343
.
8.
Franke
,
N.
,
Schreier
,
M.
, and
Kaiser
,
U.
,
2010
, “
The “I Designed It Myself” Effect in Mass Customization
,”
Manage. Sci.
,
56
(
1
), pp.
125
140
.
9.
Du
,
X.
,
Jiao
,
J.
, and
Tseng
,
M. M.
,
2006
, “
Understanding Customer Satisfaction in Product Customization
,”
Int. J. Adv. Manuf. Technol.
,
31
(
3
), pp.
396
406
.
10.
Huffman
,
C.
, and
Kahn
,
B. E.
,
1998
, “
Variety for Sale: Mass Customization or Mass Confusion?
,”
J Retail.
,
74
(
4
), pp.
491
513
.
11.
Piller
,
F.
,
Schubert
,
P.
,
Koch
,
M.
, and
Möslein
,
K.
,
2017
, “
Overcoming Mass Confusion: Collaborative Customer Co-Design in Online Communities
,”
J. Comput.-Mediat. Commun.
,
10
(
4)
.
12.
Wang
,
Y.
, and
Tseng
,
M. M.
,
2011
, “
Adaptive Attribute Selection for Configurator Design via Shapley Value
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
25
(
2
), pp.
185
195
.
13.
Liu
,
A.
,
Zhang
,
D.
,
Wang
,
X.
, and
Xu
,
X.
,
2021
, “
Blockchain-Based Customization Towards Decentralized Consensus on Product Requirement, Quality, and Price
,”
Manuf. Lett.
,
27
, pp.
18
25
.
14.
Wang
,
Y.
,
Luo
,
L.
, and
Liu
,
H.
,
2022
, “
Bridging the Semantic Gap Between Customer Needs and Design Specifications Using User-Generated Content
,”
IEEE Trans. Eng. Manage.
,
69
(
4
), pp.
1622
1634
.
15.
Lin
,
Y.
,
Yu
,
S.
,
Zheng
,
P.
,
Qiu
,
L.
,
Wang
,
Y.
, and
Xu
,
X.
,
2017
, “
VR-based Product Personalization Process for Smart Products
,”
Procedia Manuf.
,
11
, pp.
1568
1576
.
16.
Dellaert
,
B. G. C.
, and
Dabholkar
,
P. A.
,
2009
, “
Increasing the Attractiveness of Mass Customization: The Role of Complementary On-line Services and Range of Options
,”
Int. J. Electron. Commer.
,
13
(
3
), pp.
43
70
.
17.
Benade
,
M. S.
,
2018
, “
Essays on Smart Customization: Towards a Better Understanding of the Customer's Perspective on Smart Customization Offers
,”
PhD dissertation
,
RWTH Aachen University
.
18.
Green
,
M. G.
,
Tan
,
J.
,
Linsey
,
J. S.
,
Seepersad
,
C. C.
, and
Wood
,
K. L.
,
2005
, “
Effects of Product Usage Context on Consumer Product Preferences
,”
Proceedings of the ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 5a: 17th International Conference on Design Theory and Methodology
,
Long Beach, CA
,
Sept. 24–28
, pp.
171
185
.
19.
Green
,
M. G.
,
Linsey
,
J. S.
,
Seepersad
,
C. C.
,
Wood
,
K. L.
, and
Jensen
,
D. J.
,
2006
, “
Frontier Design: A Product Usage Context Method
,”
Proceedings of the ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Volume 4a: 18th International Conference on Design Theory and Methodology
,
Philadelphia, PA
,
Sept. 10–13
, pp.
99
113
.
20.
Wang
,
X.
,
Liu
,
A.
, and
Kara
,
S.
,
2022
, “
An Ontology-Based Product Usage Context Modeling Method for Smart Customization
,”
Procedia CIRP
,
109
, pp.
641
646
.
21.
Jin
,
J.
,
Liu
,
Y.
,
Ji
,
P.
, and
Kwong
,
C. K.
,
2019
, “
Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product Design
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
1
), p.
010801
.
22.
He
,
W.
,
Martinez
,
J.
,
Padhi
,
R.
,
Zhang
,
L.
, and
Ur
,
B.
,
2019
, “
When Smart Devices Are Stupid: Negative Experiences Using Home Smart Devices
,”
Proceedings of the 2019 IEEE Security and Privacy Workshops (SPW)
,
San Francisco, CA
,
May 19–23
, pp.
150
155
.
23.
Ram
,
S.
, and
Jung
,
H.-S.
,
1990
, “
The Conceptualization and Measurement of Product Usage
,”
J. Acad. Mark. Sci.
,
18
(
1
), pp.
67
76
.
24.
Belk
,
R. W.
,
1975
, “
Situational Variables and Consumer Behavior
,”
J. Consum. Res.
,
2
(
3
), pp.
157
164
.
25.
He
,
L.
,
Chen
,
W.
,
Hoyle
,
C.
, and
Yannou
,
B.
,
2012
, “
Choice Modeling for Usage Context-Based Design
,”
ASME J. Mech. Des.
,
134
(
3
), p.
031007
.
26.
Gellersen
,
H. W.
,
Schmidt
,
A.
, and
Beigl
,
M.
,
2002
, “
Multi-Sensor Context-Awareness in Mobile Devices and Smart Artifacts
,”
Mob. Netw. Appl.
,
7
(
5
), pp.
341
351
.
27.
Suryadi
,
D.
, and
Kim
,
H. M.
,
2019
, “
A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews
,”
ASME J. Mech. Des.
,
141
(
12
), p.
121104
.
28.
Li
,
X.
,
Chen
,
C.-H.
,
Zheng
,
P.
,
Jiang
,
Z.
, and
Wang
,
L.
,
2021
, “
A Context-Aware Diversity-Oriented Knowledge Recommendation Approach for Smart Engineering Solution Design
,”
Knowl.-Based Syst.
,
215
, p.
106739
.
29.
Wang
,
Z.
,
Chen
,
C.-H.
,
Li
,
X.
,
Zheng
,
P.
, and
Khoo
,
L. P.
,
2021
, “
A Context-Aware Concept Evaluation Approach Based on User Experiences for Smart Product-Service Systems Design Iteration
,”
Adv. Eng. Inform.
,
50
, p.
101394
.
30.
Wang
,
Z.
,
Chen
,
C.-H.
,
Zheng
,
P.
,
Li
,
X.
, and
Song
,
W.
,
2022
, “
A Hypergraph-Based Approach for Context-Aware Smart Product-Service System Configuration
,”
Comput. Ind. Eng.
,
163
, p.
107816
.
31.
Abu-Salih
,
B.
,
2021
, “
Domain-Specific Knowledge Graphs: A Survey
,”
J. Netw. Comput. Appl.
,
185
, p.
103076
.
32.
Li
,
X.
,
Lyu
,
M.
,
Wang
,
Z.
,
Chen
,
C.-H.
, and
Zheng
,
P.
,
2021
, “
Exploiting Knowledge Graphs in Industrial Products and Services: A Survey of key Aspects, Challenges, and Future Perspectives
,”
Comput. Ind.
,
129
, p.
103449
.
33.
Weng
,
S.-S.
,
Tsai
,
H.-J.
,
Liu
,
S.-C.
, and
Hsu
,
C.-H.
,
2006
, “
Ontology Construction for Information Classification
,”
Expert Syst. Appl.
,
31
(
1
), pp.
1
12
.
34.
Ehrlinger
,
L.
, and
Wöß
,
W.
,
2016
, “
Towards a Definition of Knowledge Graphs
,”
SEMANTiCS (Posters, Demos, SuCCESS)
,
48
(
1–4
), p.
2
.
35.
Wang
,
Q.
,
Mao
,
Z.
,
Wang
,
B.
, and
Guo
,
L.
,
2017
, “
Knowledge Graph Embedding: A Survey of Approaches and Applications
,”
IEEE Trans. Knowl. Data Eng.
,
29
(
12
), pp.
2724
2743
.
36.
Ji
,
S.
,
Pan
,
S.
,
Cambria
,
E.
,
Marttinen
,
P.
, and
Yu
,
P. S.
,
2022
, “
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
,”
IEEE Trans. Neural Netw. Learn. Syst.
,
33
(
2
), pp.
494
514
.
37.
Li
,
X.
,
Chen
,
C.-H.
,
Zheng
,
P.
,
Wang
,
Z.
,
Jiang
,
Z.
, and
Jiang
,
Z.
,
2020
, “
A Knowledge Graph-Aided Concept–Knowledge Approach for Evolutionary Smart Product–Service System Development
,”
ASME J. Mech. Des.
,
142
(
10
), p.
101403
.
38.
Zhou
,
B.
,
Shen
,
X.
,
Lu
,
Y.
,
Li
,
X.
,
Hua
,
B.
,
Liu
,
T.
, and
Bao
,
J.
,
2022
, “
Semantic-Aware Event Link Reasoning Over Industrial Knowledge Graph Embedding Time Series Data
,”
Int. J. Prod. Res.
, pp.
1
18
.
39.
Ko
,
H.
,
Witherell
,
P.
,
Lu
,
Y.
,
Kim
,
S.
, and
Rosen
,
D. W.
,
2021
, “
Machine Learning and Knowledge Graph Based Design Rule Construction for Additive Manufacturing
,”
Addit. Manuf.
,
37
, p.
101620
.
40.
Liu
,
A.
,
Zhang
,
D.
,
Wang
,
Y.
, and
Xu
,
X.
,
2022
, “
Knowledge Graph with Machine Learning for Product Design
,”
CIRP Ann.
,
71
(
1
), pp.
117
120
.
41.
Gero
,
J.
, and
Milovanovic
,
J.
,
2021
, “
The Situated Function-Behavior-Structure co-Design Model
,”
CoDesign
,
17
(
2
), pp.
211
236
.
42.
Delpeuch
,
A.
,
2019
, “Opentapioca: Lightweight Entity Linking for Wikidata,” arXiv preprint arXiv:1904.09131.
43.
Ireland
,
R.
, and
Liu
,
A.
,
2018
, “
Application of Data Analytics for Product Design: Sentiment Analysis of Online Product Reviews
,”
CIRP J. Manuf. Sci. Technol.
,
23
, pp.
128
144
.
44.
“TextRazor: Technology,” https://www.textrazor.com/technology
45.
Dale
,
R.
,
2018
, “
Text Analytics APIs, Part 2: The Smaller Players
,”
Nat. Lang. Eng.
,
24
(
5
), pp.
797
803
.
46.
Camburn
,
B.
,
He
,
Y.
,
Raviselvam
,
S.
,
Luo
,
J.
, and
Wood
,
K.
,
2020
, “
Machine Learning-Based Design Concept Evaluation
,”
ASME J. Mech. Des.
,
142
(
3
), p.
031113
.
47.
Safavi
,
T.
,
Belth
,
C.
,
Faber
,
L.
,
Mottin
,
D.
,
Müller
,
E.
, and
Koutra
,
D.
,
2019
, “
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
,”
Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM)
,
Beijing, China
,
Nov. 8–11
, pp.
528
537
.
48.
Miller
,
J. J.
,
2013
, “
Graph Database Applications and Concepts With Neo4j
,”
Proceedings of the Southern Association for Information Systems Conference
,
Atlanta, GA
,
Mar. 8–9
, Vol. 2324, No. 36.
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