Abstract

The recent development in engineering design has incorporated customer preferences by involving a choice model. In generating a choice model to produce a good quality estimate of parameters related to product attributes, a high-quality choice set is essential. However, the choice set data are often not available. This research proposes a methodology that utilizes online data and customer reviews to construct customer choice sets in the absence of both the actual choice set and the customer sociodemographic data. The methodology consists of three main parts, i.e., clustering the products based on their attributes, clustering the customers based on their reviews, and constructing the choice sets based on a sampling probability scenario that relies on product and customer clusters. The proposed scenario is called Normalized, which multiplies the product cluster and customer cluster fractions to obtain the probability sampling distribution. There are two utility functions proposed, i.e., a linear combination of product attributes only and a function that includes the interactions of product attributes and customer reviews. The methodology is implemented to a data set of laptops. The Normalized scenario performs significantly better than the baseline, Random, in predicting the test set data. Moreover, the inclusion of customer reviews into the utility function also significantly increases the predictive ability of the model. The research shows that using the product attribute data and customer reviews to construct choice sets generates choice models with higher predictive ability than randomly constructed choice sets.

References

References
1.
Li
,
H.
, and
Azarm
,
S.
,
2000
, “
Product Design Selection Under Uncertainty and With Competitive Advantage
,”
ASME J. Mech. Des.
,
122
(
4
), pp.
411
418
. 10.1115/1.1311788
2.
Kumar
,
D.
,
Chen
,
W.
, and
Simpson
,
T. W.
,
2009
, “
A Market-Driven Approach to Product Family Design
,”
Int. J. Prod. Res.
,
47
(
1
), pp.
71
104
. 10.1080/00207540701393171
3.
Michalek
,
J.
,
Ebbes
,
P.
,
Adigüzel
,
F.
,
Feinberg
,
F.
, and
Papalambros
,
P.
,
2011
, “
Enhancing Marketing With Engineering: Optimal Product Line Design for Heterogeneous Markets
,”
Int. J. Res. Market.
,
28
(
3
), pp.
1
12
. 10.1016/j.ijresmar.2010.08.001
4.
He
,
L.
,
Chen
,
W.
,
Hoyle
,
C.
, and
Yannou
,
B.
,
2012
, “
Choice Modeling for Usage Context-Based Design
,”
ASME J. Mech. Des.
,
134
(
3
), p.
0310071
. 10.1115/1.4005860
5.
Morrow
,
W. R.
,
Long
,
M.
, and
MacDonald
,
E. F.
,
2014
, “
Market-System Design Optimization With Consider-Then-Choose Models
,”
ASME J. Mech. Des.
,
136
(
3
), p.
0310031
. 10.1115/1.4026094
6.
Train
,
K. E.
,
2003
,
Discrete Choice Methods With Simulation
,
Cambridge University Press
,
Cambridge, UK
.
7.
Wang
,
M.
, and
Chen
,
W.
,
2015
, “
A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design
,”
ASME J. Mech. Des.
,
137
(
7
), p.
0714101
. 10.1115/1.4030160
8.
Chen
,
W.
,
Wassenaar
,
H. J.
, and
Hoyle
,
C.
,
2013
,
Decision-Based Design: Integrating Consumer Preferences Into Engineering Design
,
Springer-Verlag
,
London
.
9.
Decker
,
R.
, and
Trusov
,
M.
,
2010
, “
Estimating Aggregate Consumer Preferences From Online Product Reviews
,”
Int. J. Res. Market.
,
27
(
4
), pp.
293
307
. 10.1016/j.ijresmar.2010.09.001
10.
McFadden
,
D.
,
1978
, “
Modeling the Choice of Residential Location
,”
Transp. Res. Rec.
, (
673
), pp.
72
77
.
11.
Kang
,
S.
,
2018
, “
Warehouse Location Choice: A Case Study in Los Angeles, CA
,”
J. Transport Geogr
10.1016/j.jtrangeo.2018.08.007.
12.
Ioannides
,
Y. M.
, and
Zabel
,
J. E.
,
2008
, “
Interactions, Neighborhood Selection and Housing Demand
,”
J. Urban Econ.
,
63
(
1
), pp.
229
252
. 10.1016/j.jue.2007.01.010
13.
Peters
,
T.
,
Adamowicz
,
W. L.
, and
Boxall
,
P. C.
,
1995
, “
Influence of Choice Set Considerations in Modeling the Benefits From Improved Water Quality
,”
Water. Resour. Res.
,
31
(
7
), pp.
1781
1787
. 10.1029/95WR00975
14.
Valencia-Romero
,
A.
, and
Lugo
,
J. E.
,
2017
, “
An Immersive Virtual Discrete Choice Experiment for Elicitation of Product Aesthetics Using Gestalt Principles
,”
Des. Sci.
,
3
, p.
e11
. 10.1017/dsj.2017.12
15.
Gensch
,
D. H.
,
1987
, “
A Two-Stage Disaggregate Attribute Choice Model
,”
Market. Sci.
,
6
(
3
), pp.
223
239
. 10.1287/mksc.6.3.223
16.
Shocker
,
A. D.
,
Ben-Akiva
,
M.
,
Boccara
,
B.
, and
Nedungadi
,
P.
,
1991
, “
Consideration Set Influences on Consumer Decision-Making and Choice: Issues, Models, and Suggestions
,”
Market. Lett.
,
2
(
3
), pp.
181
197
10.1007/BF02404071.
17.
Jurafsky
,
D.
, and
Martin
,
J. H.
,
2009
,
Speech and Language Processing
, 2nd ed.,
Pearson Education Inc.
,
Upper Saddle River, NJ
.
18.
Levy
,
O.
, and
Goldberg
,
Y.
,
2014
, “
ependency-Based Word Embeddings
,”
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Vol. 2: Short Papers)
,
Association for Computational Linguistics
,
June 23–25
,
Baltimore, MD
, pp.
302
308
.
19.
Somprasertsri
,
G.
,
2010
, “
Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization
,”
J. Universal Comput. Sci.
,
16
(
6
), pp.
938
955
.
20.
Suryadi
,
D.
, and
Kim
,
H.
,
2018
, “
A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank
,”
ASME J. Mech. Des.
,
140
(
12
), p.
1214031
. 10.1115/1.4040913
21.
Mikolov
,
T.
,
Chen
,
K.
,
Corrado
,
G.
, and
Dean
,
J.
,
2013
, “
Efficient Estimation of Word Representations in Vector Space
,” CoRR, abs/1301.3781, https://arxiv.org/abs/1301.3781, Accessed September 21, 2016.
22.
Mikolov
,
T.
,
Sutskever
,
I.
,
Chen
,
K.
,
Corrado
,
G.
, and
Dean
,
J.
,
2013
, “
Distributed Representations of Words and Phrases and Their Compositionality
,” CoRR, abs/1310.4546, http://arxiv.org/abs/1310.4546, Accessed September 18, 2016.
23.
Rong
,
X.
,
2014
, “
word2vec Parameter Learning Explained
,” CoRR, abs/1411.2738, http://arxiv.org/abs/1411.2738, Accessed August 8, 2018.
24.
Cambria
,
E.
,
Poria
,
S.
,
Bajpai
,
R.
, and
Schuller
,
B. W.
,
2016
, “
Senticnet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
,” COLING 2016,
26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers
,
Dec. 11–16
,
Osaka, Japan
, pp.
2666
2677
.
25.
Dominick
,
J. R.
,
1999
, “
Who Do You Think You Are? Personal Home Pages and Self-Presentation on the World Wide Web
,”
J. Mass Commun. Q.
,
76
(
4
), pp.
646
658
. 10.1177/107769909907600403
26.
Gosling
,
S. D.
,
Augustine
,
A. A.
,
Vazire
,
S.
,
Holtzman
,
N.
, and
Gaddis
,
S.
,
2011
, “
Manifestations of Personality in Online Social Networks: Self-Reported Facebook-Related Behaviors and Observable Profile Information
,”
Cyberpsychol., Behav. Soc. Network.
,
14
(
9
), pp.
483
488
. 10.1089/cyber.2010.0087
27.
Nosko
,
A.
,
Wood
,
E.
, and
Molema
,
S.
,
2010
, “
All About Me: Disclosure in Online Social Networking Profiles: The Case of Facebook
,”
Comput. Hum. Behav.
,
26
(
3
), pp.
406
418
10.1016/j.chb.2009.11.012.
28.
Li
,
J.
, and
Chignell
,
M.
,
2010
, “
Birds of a Feather: How Personality Influences Blog Writing and Reading
,”
Int. J. Hum. Comput. Stud.
,
68
(
9
), pp.
589
602
10.1016/j.ijhcs.2010.04.001.
29.
Wagner
,
C.
,
Asur
,
S.
, and
Hailpern
,
J.
,
2013
, “
Religious Politicians and Creative Photographers: Automatic User Categorization in Twitter
,”
Proceedings of the 2013 International Conference on Social Computing
,
IEEE
,
Sept. 8–14
,
Alexandria, VA
, pp.
303
310
.
30.
Marriott
,
T. C.
, and
Buchanan
,
T.
,
2014
, “
The True Self Online: Personality Correlates of Preference for Self-Expression Online, and Observer Ratings of Personality Online and Offline
,”
Comput. Hum. Behav.
,
32
, pp.
171
177
. 10.1016/j.chb.2013.11.014
31.
Pelleg
,
D.
, and
Moore
,
A. W.
,
2000
, “
X-Means: Extending k-Means With Efficient Estimation of the Number of Clusters
,”
Proceedings of the Seventeenth International Conference on Machine Learning
, ICML ’00,
Morgan Kaufmann Publishers Inc.
,
San Francisco, CA
,
June 29–July 2
, Stanford, CA, pp.
727
734
.
32.
Bigi
,
B.
,
2003
, “Using Kullback-Leibler Distance for Text Categorization,”
Advances in Information Retrieval
,
F.
Sebastiani
, ed.,
Springer
,
Berlin, Heidelberg
, pp.
305
319
.
33.
Bird
,
S.
,
Klein
,
E.
, and
Loper
,
E.
,
2009
,
Natural Language Processing With Python
, 1st ed,
O’Reilly Media, Inc.
,
Sebastopol, CA
.
34.
Manning
,
C. D.
,
Surdeanu
,
M.
,
Bauer
,
J.
,
Finkel
,
J.
,
Bethard
,
S. J.
, and
McClosky
,
D.
,
2014
, “
The Stanford CoreNLP Natural Language Processing Toolkit
,”
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics (ACL) System Demonstrations
,
June 23–24
,
Baltimore, MD
, pp.
55
60
.
35.
Novikov
,
A.
,
2018
, Annoviko/Pyclustering: Pyclustering 0.8.1 Release,
May
.
36.
Řehůřek
,
R.
, and
Sojka
,
P.
,
2010
, “
Software Framework for Topic Modelling With Large Corpora
,”
Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA
,
Valletta, Malta
,
May 22
, pp.
46
50
. See also URL http://is.muni.cz/publication/884893/en
37.
Brathwaite
,
T.
, and
Walker
,
J. L.
,
2018
, “
Asymmetric, Cosed-Form, Finite-Parameter Models of Multinomial Choice
,”
J. Choice Modell.
,
29
, pp.
78
112
.
38.
Jones
,
E.
,
Oliphant
,
T.
, and
Peterson
,
P.
,
2001
, SciPy: Open Source Scientific Tools for Python, http://www.scipy.org/, Accessed January 11, 2019.
39.
Koren
,
Y.
,
Bell
,
R.
, and
Volinsky
,
C.
,
2009
, “
Matrix Factorization Techniques for Recommender Systems
,”
Computer
,
42
(
8
), pp.
30
37
. 10.1109/MC.2009.263
You do not currently have access to this content.