This paper presents a systematic function recommendation process (FRP) to recommend new functions to an existing product and service. Function plays a vital role in mapping user needs to design parameters (DPs) under constraints. It is imperative for manufacturers to continuously equip an existing product/service with exciting new functions. Traditionally, functions are mostly formulated by experienced designers and senior managers based on their subjective experience, knowledge, creativity, and even heuristics. Nevertheless, against the sweeping trend of information explosion, it is increasingly inefficient and unproductive for designers to manually formulate functions. In e-commerce, recommendation systems (RS) are ubiquitously used to recommend new products to users. In this study, the practically viable recommendation approaches are integrated with the theoretically sound design methodologies to serve a new paradigm of recommending new functions to an existing product/service. The aim is to address the problem of how to estimate an unknown rating that a target user would give to a candidate function that is not carried by the target product/service yet. A systematic function → product recommendation process is prescribed, followed by a detailed case study. It is indicated that practically meaningful functional recommendations (FRs) can indeed by generated through the proposed FRP.

References

References
1.
Suh
,
N. P.
,
2001
,
Axiomatic Design: Advances and Applications
(The Oxford Series on Advanced Manufacturing), Oxford University Press, Oxford, UK.
2.
Goel
,
A. K.
,
Rugaber
,
S.
, and
Vattam
,
S.
,
2009
, “
Structure, Behavior, and Function of Complex Systems: The Structure, Behavior, and Function Modeling Language
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
23
(
1
), pp.
23
35
.
3.
Akao
,
Y.
, and
Mazur
,
G. H.
,
2003
, “
The Leading Edge in QFD: Past, Present and Future
,”
Int. J. Qual. Reliab. Manage.
,
20
(
1
), pp.
20
35
.
4.
Pahl
,
G.
,
Beitz
,
W.
,
Feldhusen
,
J.
, and
Grote
,
K. H.
,
2007
,
Engineering Design: A Systematic Approach
,
3rd ed.
,
Springer
,
Berlin
.
5.
Dorst
,
K.
, and
Cross
,
N.
,
2001
, “
Creativity in the Design Process: Co-Evolution of Problem-Solution
,”
Des. Stud.
,
22
(
5
), pp.
425
437
.
6.
Liu
,
A.
, and
Lu
,
S. C. Y.
,
2015
, “
A New Coevolution Process for Conceptual Design
,”
CIRP Ann.-Manuf. Technol.
,
64
(
1
), pp.
153
156
.
7.
Dorst
,
K.
,
2011
, “
The Core of ‘Design Thinking’ and Its Application
,”
Des. Stud.
,
32
(
6
), pp.
521
532
.
8.
Li
,
J.
,
Tao
,
F.
,
Cheng
,
Y.
, and
Zhao
,
L.
,
2015
, “
Big Data in Product Lifecycle Management
,”
Int. J. Adv. Manuf. Technol.
,
81
(
1–4
), pp.
667
684
.
9.
Gediminas
,
A.
, and
Tuzhilin
,
A.
,
2005
, “
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
,”
IEEE Trans. Knowl. Data Eng.
,
17
(
6
), pp.
734
749
.
10.
Francesco
,
R.
,
Rokach
,
L.
, and
Shapira
,
B.
,
2011
,
Introduction to Recommender Systems Handbook
,
Springer
,
New York
.
11.
Fuge
,
M.
,
Peters
,
B.
, and
Agogino
,
A.
,
2014
, “
Machine Learning Algorithms for Recommending Design Methods
,”
ASME J. Mech. Des.
,
136
(
10
), p.
101103
.
12.
Rodenacker
,
W.
,
1971
,
Methodisches Konstruieren
,
Springer-Verlag
,
Berlin
.
13.
Erden
,
M. S.
,
Komoto
,
H.
,
van Beek
,
T. J.
,
D'Amelio
,
V.
,
Echavarria
,
E.
, and
Tomiyama
,
T.
,
2008
, “
A Review of Function Modeling: Approaches and Applications
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
22
(
2
), pp.
147
169
.
14.
Welch
,
R. V.
, and
Dixon
,
J. R.
,
1994
, “
Guiding Conceptual Design Through Behavioral Reasoning
,”
Res. Eng. Des.
,
6
(
3
), pp.
169
188
.
15.
Deng
,
Y. M.
,
2002
, “
Function and Behavior Representation in Conceptual Mechanical Design
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
16
(5), pp.
343
362
.
16.
Chen
,
Y.
,
Zhang
,
Z. N.
,
Huang
,
J.
, and
Xie
,
Y. B.
,
2013
, “
Toward a Scientific Ontology Based Concept of Function
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
27
(
3
), pp.
241
248
.
17.
Gero
,
J. S.
,
1990
, “
Design Prototypes: A Knowledge Representation Schema for Design
,”
AI Mag.
,
11
(
4
), pp.
26
36
.
18.
Kannengiesser
,
U.
, and
Gero
,
J. S.
,
2004
, “
The Situated Function-Behavior-Structure Framework
,”
Des. Stud.
,
25
(
4
), pp.
373
391
.
19.
Umeda
,
Y.
,
Ishii
,
M.
,
Yoshioka
,
M.
,
Shimomura
,
Y.
, and
Tomiyama
,
T.
,
1996
, “
Supporting Conceptual Design Based on the Function-Behavior-State Modeler
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
10
(
4
), pp.
275
288
.
20.
Chakrabarti
,
A.
,
Shea
,
K.
,
Stone
,
R.
,
Cagan
,
J.
,
Campbell
,
M.
,
Hernandez
,
N. V.
, and
Wood
,
K. L.
,
2011
, “
Computer-Based Design Synthesis Research: An Overview
,”
ASME J. Comput. Inf. Sci. Eng.
,
11
(
2
), p.
021003
.
21.
Stone
,
R. B.
, and
Wood
,
K. L.
,
2000
, “
Development of a Functional Basis for Design
,”
ASME J. Mech. Des.
,
122
(
4
), pp.
359
370
.
22.
Lu
,
S. C. Y.
, and
Liu
,
A.
,
2011
, “
Subjectivity and Objectivity in Design Decisions
,”
CIRP Ann.-Manuf. Technol.
,
60
(
1
), pp.
161
164
.
23.
Eckert
,
C.
,
Alink
,
T.
,
Ruckpaul
,
A.
, and
Albers
,
A.
,
2011
, “
Different Notions of Function: Results From an Experiment on the Analysis of an Existing Product
,”
J. Eng. Des.
,
22
(
11–12
), pp.
811
837
.
24.
Vermaas
,
P. E.
,
2013
, “
The Coexistence of Engineering Meanings of Function: Four Responses and Their Methodological Implications
,”
Artif. Intell. Eng. Des., Anal. Manuf.
,
27
(
03
), pp.
191
202
.
25.
Maier
,
J. R. A.
, and
Fadel
,
G. M.
,
2009
, “
Affordance-Based Design: A Relational Theory for Design
,”
Res. Eng. Des.
,
20
(
1
), pp.
13
27
.
26.
Maier
,
J. R. A.
, and
Fadel
,
G. M.
,
2009
, “
Affordance-Based Design Methods for Innovative Design, Redesign and Reverse Engineering
,”
Res. Eng. Des.
,
20
(
4
), pp.
225
239
.
27.
Ciavola
,
B. T.
,
Wu
,
C. L.
, and
Gershenson
,
J. K.
,
2015
, “
Integrating Function-and-Affordance-Based Design Representations
,”
ASME J. Mech. Des.
,
137
(
5
), p.
051101
.
28.
Nikolaus
,
F.
,
Von Hippel
,
E.
, and
Schreier
,
M.
,
2006
, “
Finding Commercially Attractive User Innovations: A Test of Lead-User Theory
,”
J. Prod. Innovation Manage.
,
23
(
4
), pp.
301
315
.
29.
Sutton
,
R. I.
, and
Hargadon
,
A.
,
1996
, “
Brainstorming Groups in Context: Effectiveness in a Product Design Firm
,”
Administrative Sci. Q.
,
41
(
4
), pp.
685
718
.
30.
Christina
,
W.
,
2000
, “
Ethnography in the Field of Design
,”
Hum. Organ.
,
59
(
4
), pp.
377
388
.
31.
Schafer
,
J. B.
,
Frankowski
,
D.
,
Herlocker
,
J.
, and
Sen
,
S.
,
2007
, “
Collaborative Filtering Recommender Systems
,”
The Adaptive Web
,
Springer
,
Berlin
, pp.
291
324
.
32.
Jesús
,
B.
,
Ortega
,
F.
,
Hernando
,
A.
, and
Gutiérrez
,
A.
,
2013
, “
Recommender Systems Survey
,”
Knowl.-Based Syst.
,
46
, pp.
109
132
.
33.
Shah
,
J. J.
,
Steve
,
M. S.
, and
Vargas-Hernandez
,
N.
,
2003
, “
Metrics for Measuring Ideation Effectiveness
,”
Des. Stud.
,
24
(
2
), pp.
111
134
.
34.
Lu
,
S. C. Y.
, and
Liu
,
A.
,
2012
, “
Abductive Reasoning for Design Synthesis
,”
CIRP Ann.-Manuf. Technol.
,
61
(
1
), pp.
143
146
.
35.
McAdams
,
D. A.
, and
Wood
,
K. L.
,
2002
, “
A Quantitative Similarity Metric for Design-by-Analogy
,”
ASME J. Mech. Des.
,
124
(
2
), pp.
173
182
.
36.
Sim
,
S. K.
, and
Duffy
,
A. H. B.
,
2003
, “
Towards an Ontology of Generic Engineering Design Activities
,”
Res. Eng. Des.
,
14
(
4
), pp.
200
223
.
37.
Mudambi
,
S. M.
, and
Schuff
,
D.
,
2010
, “
What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com
,”
MIS Q.
,
34
(
1
), pp.
185
200
.
38.
Liu
,
Y.
,
Jin
,
J.
,
Ji
,
P.
,
Harding
,
J. A.
, and
Fung
,
R. Y.
,
2013
, “
Identifying Helpful Online Reviews: A Product Designer's Perspective
,”
Comput.-Aided Des.
,
45
(
2
), pp.
180
194
.
39.
Pazzani
,
M. J.
, and
Billsus
,
D.
,
2007
, “
Content-Based Recommendation Systems
,”
The Adaptive Web
,
Springer
,
Berlin
, pp.
325
341
.
40.
Liu
,
A.
, and
Lu
,
S. C. Y.
,
2016
, “
A Crowdsourcing Design Framework for Concept Generation
,”
CIRP Ann.-Manuf. Technol.
,
65
(
1
), pp.
177
180
.
41.
Liu
,
B.
,
Hu
,
M. Q.
, and
Cheng
,
J. S.
,
2005
, “
Opinion Observer: Analyzing and Comparing Opinions on the Web
,”
14th International Conference on World Wide Web
(
WWW
), Chiba, Japan, May 10–14, pp.
342
351
.
42.
Jin
,
J.
,
Liu
,
Y.
,
Ji
,
P.
, and
Liu
,
H. G.
,
2016
, “
Understanding Big Consumer Opinion Data for Market-Driven Product Design
,”
Int. J. Prod. Res.
,
54
(
10
), pp.
3019
3041
.
43.
Hu
,
M. Q.
, and
Liu
,
B.
,
2004
, “
Mining Opinion Features in Customer Reviews
,”
AAAI J.
,
4
(
4
), pp.
755
760
.
44.
Lorenzi
,
F.
, and
Francesco
,
R.
,
2005
, “
Case-Based Recommender Systems: A Unifying View
,”
Intelligent Techniques for Web Personalization
,
Springer
,
Berlin
, pp.
89
113
.
45.
Xavier
,
A.
,
Pujol
,
J. M.
,
Tintarev
,
N.
, and
Oliver
,
N.
,
2009
, “
Rate It Again: Increasing Recommendation Accuracy by User Re-Rating
,”
Third ACM Conference on Recommender Systems
(
RecSys
), New York, Oct. 23–25, pp.
173
180
.
46.
Xavier
,
A.
,
Pujol
,
J. M.
, and
Oliver
,
N.
,
2009
, “
I Like It… I Like It Not: Evaluating User Ratings Noise in Recommender Systems
,”
International Conference on User Modeling, Adaptation, and Personalization
(
UMAP
), Trento, Italy, June 22–26, pp.
247
258
.
47.
Xavier
,
A.
,
Lathia
,
N.
,
Pujol
,
J. P.
,
Kwak
,
H.
, and
Oliver
,
N.
,
2009
, “
The Wisdom of the Few: A Collaborative Filtering Approach Based on Expert Opinions From the Web
,”
32nd International ACM SIGIR Conference on Research and Development in Information Retrieval
(
SIGIR
), pp.
532
539
.
48.
Pajo
,
S. J.
,
Verhaegen
,
P. A.
,
Vandevenne
,
D.
, and
Duflou
,
J. R.
,
2013
, “
Analysis of Automatic Online Lead User Identification
,”
Smart Product Engineering
,
Springer
,
Berlin
, pp.
505
514
.
49.
Gediminas
,
A.
, and
Kwon
,
Y.
,
2007
, “
New Recommendation Techniques for Multicriteria Rating Systems
,”
IEEE Intell. Syst.
,
22
(
3
), pp.
48
55
.
50.
Anderson
,
C.
,
2006
,
The Long Tail: Why the Future of Business is Selling Less of More
,
Hachette Books
,
New York
.
51.
Elberse
,
A.
,
2008
, “
Should You Invest in the Long Tail?
,”
Harv. Bus. Rev.
,
86
(
7/8
), p.
88
.
52.
Savransky
,
S. D.
,
2000
,
Engineering of Creativity: Introduction to TRIZ Methodology of Inventive Problem Solving
,
CRC Press
,
Boca Raton, FL
.
53.
Purcell
,
A. T.
, and
Gero
,
J. S.
,
1996
, “
Design and Other Types of Fixation
,”
Des. Stud.
,
17
(
4
), pp.
363
383
.
54.
Yu
,
X. C.
,
Zhao
,
T. H.
, and
Tong
,
S. S.
,
2017
, “
Development Report on China's WeChat in 2014
,”
Development Report on China's New Media
,
Springer
,
Singapore
, pp.
63
78
.
55.
Tseng
,
M. M.
,
Jiao
,
R. J.
, and
Wang
,
C.
,
2010
, “
Design for Mass Personalization
,”
CIRP Ann.-Manuf. Technol.
,
59
(
1
), pp.
175
178
.
56.
Gediminas
,
A.
, and
Tuzhilin
,
A.
,
2015
, “
Context-Aware Recommender Systems
,”
Recommender Systems Handbook
,
Springer
,
New York
, pp.
191
226
.
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