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Journal articles on the topic 'Choice modelling'

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Published: 4 June 2021
Last updated: 21 February 2023

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1

Roberts, D. R. "Modelling customer choice." Journal of Revenue and Pricing Management 1, no. 4 (January 2003): 369–78. http://dx.doi.org/10.1057/palgrave.rpm.5170040.

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2

Huybers, Twan. "Destination Choice Modelling: What's in a Name?" Tourism Economics 11, no. 3 (September 2005): 329–50. http://dx.doi.org/10.5367/000000005774352999.

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Discrete choice modelling can be employed to analyse tourists' holiday destination choices. The data for a destination choice modelling analysis are often obtained from a survey in which prospective tourists indicate their choices in a series of hypothetical scenarios. In that context, an issue deserving investigation is the use of destination names in the stated choice task. Using the names of destinations to label the alternatives in a choice scenario would be expected to enhance the predictive validity of the choice model. On the other hand, the experimental design of the choice scenarios may yield destination descriptions that appear unrealistic to survey respondents. In this paper, the labelling issue is investigated using a choice modelling study of short-break destination choices by residents from Melbourne, Australia. The multinomial logit model is used to compare the results of labelled and unlabelled stated choice tasks. The comparative results are reported and discussed and implications for destination choice modelling applications are drawn.
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3

Huybers, Twan. "Domestic tourism destination choices ? a choice modelling analysis." International Journal of Tourism Research 5, no. 6 (2003): 445–59. http://dx.doi.org/10.1002/jtr.450.

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4

Thill, Jean-Claude. "Choice set formation for destination choice modelling." Progress in Human Geography 16, no. 3 (September 1992): 361–82. http://dx.doi.org/10.1177/030913259201600303.

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5

Fotheringham, A. S. "Modelling Hierarchical Destination Choice." Environment and Planning A: Economy and Space 18, no. 3 (March 1986): 401–18. http://dx.doi.org/10.1068/a180401.

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The production-constrained gravity formulation is shown to be an especially inaccurate specification of reality whenever the selection of destinations by individuals results from a hierarchical choice process. Hierarchical decisionmaking violates the Independence from Irrelevant Alternatives property embedded in the theoretical derivation of the production-constrained gravity model from choice axioms. Various aspects of gravity model misspecification resulting from hierarchical destination choice are investigated and an empirical example is given in terms of US migrants. A discussion is presented of several destination choice models that are more accurately specified than the gravity formulation when destination choice is hierarchical. The recently derived competing destinations formulation is shown to be amongst the most useful in this respect. The discussion is framed in the context of discrete choice theory.
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6

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. "Discrete Choice Modelling (Teil I)." WiSt - Wirtschaftswissenschaftliches Studium 37, no. 7 (2008): 356–62. http://dx.doi.org/10.15358/0340-1650-2008-7-356.

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7

Gönsch, Jochen, Robert Klein, and Claudius Steinhardt. "Discrete Choice Modelling (Teil II)." WiSt - Wirtschaftswissenschaftliches Studium 37, no. 8 (2008): 412–18. http://dx.doi.org/10.15358/0340-1650-2008-8-412.

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8

Li, Dawei, Tomio Miwa, and Takayuki Morikawa. "Considering En-Route Choices in Utility-Based Route Choice Modelling." Networks and Spatial Economics 14, no. 3-4 (September 11, 2014): 581–604. http://dx.doi.org/10.1007/s11067-014-9255-5.

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9

Timmermans, H., and P. van der Waerden. "Modelling Sequential Choice Processes: The Case of Two-Stop Trip Chaining." Environment and Planning A: Economy and Space 24, no. 10 (October 1992): 1483–90. http://dx.doi.org/10.1068/a241483.

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Traditional decompositional preferences and choice studies are focused on the prediction of single choices, such as choice of shopping centre or transport mode. Discrete choice experiments are used to derive choice models that predict the probability of choosing a choice alternative as a function of its attributes. In this paper these traditional models are extended by addressing the problem of sequential choice behaviour. It is demonstrated how discrete choice experiments and universal logit models may be used to predict a choice sequence. The approach is illustrated for the problem of trip chaining. The research findings support the suggested approach.
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10

Rungie, Campbell M., Leonard V. Coote, and Jordan J. Louviere. "Structural Choice Modelling: Theory and Applications to Combining Choice Experiments." Journal of Choice Modelling 4, no. 3 (2011): 1–29. http://dx.doi.org/10.1016/s1755-5345(13)70040-x.

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11

Arencibia, Ana Isabel, María Feo-Valero, Leandro García-Menéndez, and Concepción Román. "Modelling mode choice for freight transport using advanced choice experiments." Transportation Research Part A: Policy and Practice 75 (May 2015): 252–67. http://dx.doi.org/10.1016/j.tra.2015.03.027.

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12

Oral, Muhittin, and Ossama Kettani. "Modelling the Process of Multiattribute Choice." Journal of the Operational Research Society 40, no. 3 (March 1989): 281. http://dx.doi.org/10.2307/2583341.

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13

Beheshtitabar, Ehsan, Sergi Aguilar Ríos, David König-Hollerwöger, Zdeněk Svatý, and Clas Rydergren. "ROUTE CHOICE MODELLING FOR BICYCLE TRIPS." INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING 4, no. 2 (June 2014): 194–209. http://dx.doi.org/10.7708/ijtte.2014.4(2).06.

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14

Bashir, Jasmeena, Sarafaraz Equbal, and Bisma Raina. "The Choice Modelling Approach: A Review." ECS Transactions 107, no. 1 (April 24, 2022): 15081–97. http://dx.doi.org/10.1149/10701.15081ecst.

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Among the set of stated preference methods, the Choice Experiments (CES) is now widely applied to diverse environmental goods where market failure exists. This method has found its success in resolving some methodological issues of Contingent Valuation Method (CVM). Choice experiment (CE) is the method which has greatest number of applications where people select the best favored substitute among the set of substitutes by simply ranking and the rating scales. Despite many limitations or methodological issues arising due to hypothetical nature of stated preference surveys, this method could be successfully applicable to a series of non-marketed properties with certainty to measure consumer preferences. The main aim of this study is to analyze the recent development of the CEs and its methodological issues. Besides this the review will also include background and theoretical basis of Choice Experiments.
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15

Gostilovich, A. O. "Consumer Choice Modelling in Sharing Economy." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 1153–61. http://dx.doi.org/10.47059/revistageintec.v11i2.1744.

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Development of sharing economy creates new challenges and opens unprecedented business opportunities. In this economic environment, industrial enterprises can expand their direct selling strategies with the new business model “product as a service”. This option is the result of a shift in consumer preferences among clients of industrial enterprises. The development of the consumer choice model applied to sharing economy is a topical agenda, perhaps now more than ever. Such a model, if available, would help predict multiple scenarios of consumer behaviour and prepare the manufacturing companies for better interaction with their target market. This article makes an attempt to offer a consumer choice model in sharing economy, based on 4 types of possible consumer behaviour. The results of the article serve as a foundation of multi-agent modelling and quantitative assessment of abstract situations in the business-to-consumer market.
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16

Oral, Muhittin, and Ossama Kettani. "Modelling the Process of Multiattribute Choice." Journal of the Operational Research Society 40, no. 3 (March 1989): 281–91. http://dx.doi.org/10.1057/jors.1989.38.

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17

Varlamov, A. A., D. L. Yakobchuk, and I. A. Lozhkin. "Choice of Factors Modelling of Concrete." IOP Conference Series: Materials Science and Engineering 661 (November 20, 2019): 012087. http://dx.doi.org/10.1088/1757-899x/661/1/012087.

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18

Wiley, James B., and Harry J. P. Timmermans. "Modelling Portfolio Choice in Transportation Research." Transport Reviews 29, no. 5 (September 2009): 569–86. http://dx.doi.org/10.1080/01441640902829462.

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19

Cardoso, Antonio, Mario de Araujo, and Eduarda Coquet. "Modelling children's choice decisions of clothing." Journal of Fashion Marketing and Management: An International Journal 12, no. 3 (July 11, 2008): 415–28. http://dx.doi.org/10.1108/13612020810889344.

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20

Van Camp, Arthur, and Enrique Miranda. "Modelling epistemic irrelevance with choice functions." International Journal of Approximate Reasoning 125 (October 2020): 49–72. http://dx.doi.org/10.1016/j.ijar.2020.06.010.

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21

Bujosa Bestard, Angel, and Antoni Riera Font. "Environmental diversity in recreational choice modelling." Ecological Economics 68, no. 11 (September 2009): 2743–50. http://dx.doi.org/10.1016/j.ecolecon.2009.05.016.

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22

Ryan, Mandy, and Diane Skåtun. "Modelling non-demanders in choice experiments." Health Economics 13, no. 4 (April 2004): 397–402. http://dx.doi.org/10.1002/hec.821.

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23

Madan, Dilip B. "Utility correlations in probabilistic choice modelling." Economics Letters 20, no. 3 (January 1986): 241–45. http://dx.doi.org/10.1016/0165-1765(86)90030-3.

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24

van Middelkoop, Manon, Aloys W. J. Borgers, and H. J. P. Timmermans. "Modelling Tourist Destination Choice Using a Decision Table Induction Algorithm." Environment and Planning A: Economy and Space 35, no. 9 (September 2003): 1669–87. http://dx.doi.org/10.1068/a35182.

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The authors report on the development and test of a rule-based model of tourist destination choice, using decision tables to represent the rules that specify the conditions under which particular destination choices are made. Rules are extracted from empirical data on tourist destination choice, collected in the Netherlands in 1998. The specification of the model and its performance are discussed.
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25

Moslem, Sarbast, Tiziana Campisi, Agnieszka Szmelter-Jarosz, Szabolcs Duleba, Kh Md Nahiduzzaman, and Giovanni Tesoriere. "Best–Worst Method for Modelling Mobility Choice after COVID-19: Evidence from Italy." Sustainability 12, no. 17 (August 22, 2020): 6824. http://dx.doi.org/10.3390/su12176824.

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All countries have suffered from the COVID-19 crisis; the pandemic has adversely impacted all sectors. In this study, we examine the transport sector with a specific focus on the problem of commuting mode choice and propose a new decision-making approach for the alternative modes after synthesizing expert opinions. As a methodology, a customized model of the recently developed best–worst method (BWM) is used to evaluate mobility choice alternatives. The survey reflects citizens’ opinions toward mobility choices in two Italian cities, Palermo and Catania, before and during the pandemic. BWM is a useful tool for examining mobility choice in big cities. The adopted model is easy to apply and capable of providing effective solutions for sustainable mode choice. The urban context is analyzed considering the importance of transport choices, evaluating the variation of resilience to the changing opinions of users.
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26

Windle, Jill, and John Rolfe. "Diversification choices in agriculture: a Choice Modelling case study of sugarcane growers." Australian Journal of Agricultural and Resource Economics 49, no. 1 (March 2005): 63–74. http://dx.doi.org/10.1111/j.1467-8489.2005.00279.x.

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27

Timmermans, H. J. P. "A Stated Choice Model of Sequential Mode and Destination Choice Behaviour for Shopping Trips." Environment and Planning A: Economy and Space 28, no. 1 (January 1996): 173–84. http://dx.doi.org/10.1068/a280173.

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Stated preference and choice models currently used in urban planning are focused on predicting single choices. In this paper the intention is to extend these modelling approaches to the case of sequential choice behaviour. Design strategies and model specifications that allow one to predict sequential choice are discussed. The approach is illustrated in a study of sequential mode and destination choice behaviour for shopping trips. The research findings suggest that the proposed approach may be a valuable extension of currently available stated preference and choice methods to analyse more complex forms of decisionmaking.
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28

Zhang, Hengyang, Jason Hawkins, and Khandker Nurul Habib. "A joint model of place of residence (POR) and place of work (POW): Making use of Gibbs sampling technique to overcome arbitrary assumptions in contexts of data limitation." Journal of Transport and Land Use 12, no. 1 (December 3, 2019): 873–92. http://dx.doi.org/10.5198/jtlu.2019.1624.

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Place or residence (POR) and place of work (POW) are two spatial pivots defining patterns of travel behavior. These choices are considered part of long-term choice influencing short-term daily travel choices. Hence, POR-POW distributions are input into almost all daily travel demand models. However, in many cases, POW-POR is modelled in an ad-hoc way considering the gravity-based or entropy is maximizing aggregate modelling approach. Lack of data on the sequence of choices related to POR and POW is often blamed for avoiding using disaggregate choice model. Recognizing such data limitation, this paper presents an alternative methodology of modelling joint distribution of POW-POW that uses disaggregate choice models without necessarily knowing the sequence of POR and POW choices. It uses the conditional probability break downs of joint POR-POW choice probabilities as depicted in the Gibbs sampling approach. This allows capturing effects of household socioeconomic characteristics, zonal land-use characteristics, and modal accessibility factors in the POR-POW models. The model is applied for a case study in the city of Ottawa. Results reveal that the proposed methodology can replicate observed patterns of POR-POW with a high degree of accuracy.
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29

Kramberger, Tomaž, Bojan Rupnik, Gregor Štrubelj, and Klemen Prah. "Port Hinterland Modelling Based on Port Choice." PROMET - Traffic&Transportation 27, no. 3 (June 26, 2015): 195–203. http://dx.doi.org/10.7307/ptt.v27i3.1611.

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This paper presents a new approach for hinterland modelling based on the results of port choice modelling. The paper follows the idea that the shippers’ port choice is a trade-off between various objective and subjective factors. The presented model tackles the problem by applying the AHP method in order to obtain ports’ preference rates based on subjective factors, and combine them with objective factors, which include port operation costs, sailing times, and land transport costs using MILP. The ports’ hinterlands are modelled by finding the optimal port of choice for different locations across Europe and merging the identical results. The model can be used in order to produce captive hinterland of ports and can also be exploited in order to analyse how changes in the traffic infrastructure influence the size of hinterlands.
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30

Neumann, Oliver, and Adrian Ritz. "Public service motivation and rational choice modelling." Public Money & Management 35, no. 5 (July 3, 2015): 365–70. http://dx.doi.org/10.1080/09540962.2015.1061179.

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31

Concu, Giovanni B. "Measuring Environmental Externality Spillovers through Choice Modelling." Environment and Planning A: Economy and Space 41, no. 1 (January 2009): 199–212. http://dx.doi.org/10.1068/a39266.

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32

Glenk, Klaus, and Sergio Colombo. "Modelling outcome-related risk in choice experiments." Australian Journal of Agricultural and Resource Economics 57, no. 4 (June 14, 2013): 559–78. http://dx.doi.org/10.1111/1467-8489.12012.

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33

Yeo, Gi-Tae, Adolf K. Y. Ng, Paul Tae-Woo Lee, and Zaili Yang. "Modelling port choice in an uncertain environment." Maritime Policy & Management 41, no. 3 (October 21, 2013): 251–67. http://dx.doi.org/10.1080/03088839.2013.839515.

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34

Fitzgerald, PE, and M. Aristides. "PMC18 STATISTICAL ISSUES IN DISCRETE CHOICE MODELLING." Value in Health 7, no. 6 (November 2004): 774. http://dx.doi.org/10.1016/s1098-3015(10)66065-2.

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35

Fischer, Manfred M., and Peter Nijkamp. "From static towards dynamic discrete choice modelling." Regional Science and Urban Economics 17, no. 1 (February 1987): 3–27. http://dx.doi.org/10.1016/0166-0462(87)90066-4.

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36

Roh, Hyuk-Jae, and Ata M. Khan. "Enhancing algorithmic base for discrete choice modelling." KSCE Journal of Civil Engineering 17, no. 7 (October 24, 2013): 1798–809. http://dx.doi.org/10.1007/s12205-013-0318-9.

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37

González-Benito, Óscar. "Overcoming data limitations for store choice modelling." Journal of Retailing and Consumer Services 9, no. 5 (September 2002): 259–68. http://dx.doi.org/10.1016/s0969-6989(01)00035-2.

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38

Crouch, Geoffrey I., Timothy M. Devinney, Jordan J. Louviere, and Towhidul Islam. "Modelling consumer choice behaviour in space tourism." Tourism Management 30, no. 3 (June 2009): 441–54. http://dx.doi.org/10.1016/j.tourman.2008.07.003.

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39

Magala, Mateus, and Adrian Sammons. "A New Approach to Port Choice Modelling." Maritime Economics & Logistics 10, no. 1-2 (March 2008): 9–34. http://dx.doi.org/10.1057/palgrave.mel.9100189.

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40

Cantillo, Víctor, and Juan de Dios Ortúzar. "Implications of Thresholds in Discrete Choice Modelling." Transport Reviews 26, no. 6 (November 2006): 667–91. http://dx.doi.org/10.1080/01441640500487275.

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41

Ottomanelli, Michele, Mauro Dell'Orco, and Domenico Sassanelli. "Modelling parking choice behaviour using Possibility Theory." Transportation Planning and Technology 34, no. 7 (October 2011): 647–67. http://dx.doi.org/10.1080/03081060.2011.602846.

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42

Grammig, Joachim, Reinhard Hujer, and Michael Scheidler. "Discrete choice modelling in airline network management." Journal of Applied Econometrics 20, no. 4 (2005): 467–86. http://dx.doi.org/10.1002/jae.799.

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43

Dibb, S. "Modelling in new housing choice—An application." Omega 22, no. 6 (November 1994): 589–600. http://dx.doi.org/10.1016/0305-0483(94)90050-7.

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44

Burkart, Christian, Pamela C. Nolz, and Walter J. Gutjahr. "Modelling beneficiaries’ choice in disaster relief logistics." Annals of Operations Research 256, no. 1 (January 9, 2016): 41–61. http://dx.doi.org/10.1007/s10479-015-2097-9.

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45

Smith, Tony E. "Spatial interaction modelling and residential choice analysis." Transportation Research Part A: General 22, no. 6 (November 1988): 470–71. http://dx.doi.org/10.1016/0191-2607(88)90052-0.

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46

Prave, Rose Sebastianelli, and J. Keith Ord. "An adaptive approach for modelling multiattribute choice." Journal of Multi-Criteria Decision Analysis 4, no. 2 (June 1995): 91–106. http://dx.doi.org/10.1002/mcda.4020040203.

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47

Vyvere, Yves Van De. "Stated preference decompositional modelling and residential choice." Geoforum 25, no. 2 (May 1994): 189–202. http://dx.doi.org/10.1016/0016-7185(94)90015-9.

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48

Collins, Andrew T., Stephane Hess, and John M. Rose. "Choice modelling with search and sort data from an interactive choice experiment." Transportation Research Part E: Logistics and Transportation Review 56 (September 2013): 36–45. http://dx.doi.org/10.1016/j.tre.2013.05.002.

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49

Hensher, D. A. "Automobile-type choice: A note on alternative specifications for discrete-choice modelling." Transportation Research Part B: Methodological 20, no. 5 (October 1986): 429–33. http://dx.doi.org/10.1016/0191-2615(86)90014-7.

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50

Pasquier, Nicolas, and Sujoy Chatterjee. "Customer Choice Modelling: A Multi-Level Consensus Clustering Approach." Annals of Emerging Technologies in Computing 5, no. 2 (April 1, 2021): 103–20. http://dx.doi.org/10.33166/aetic.2021.02.009.

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Customer Choice Modeling aims to model the decision-making process of customers, or segments of customers, through their choices and preferences identified by the analysis of their behaviors in one or more specific contexts. Clustering techniques are used in this context to identify patterns in their choices and preferences, to define segments of customers with similar behaviors, and to model how customers of different segments respond to competing products and offers. However, data clustering is an unsupervised learning task by nature, that is the grouping of customers with similar behaviors in clusters must be performed without prior knowledge about the nature and the number of intrinsic groups of data instances, i.e., customers, in the data space. Thus, the choice of both the clustering algorithm used and its parameterization, and of the evaluation method used to assess the relevance of the resulting clusters are central issues. Consensus clustering, or ensemble clustering, aims to solve these issues by combining the results of different clustering algorithms and parameterizations to generate a more robust and relevant final clustering result. We present a Multi-level Consensus Clustering approach combining the results of several clustering algorithmic configurations to generate a hierarchy of consensus clusters in which each cluster represents an agreement between different clustering results. A closed sets based approach is used to identified relevant agreements, and a graphical hierarchical representation of the consensus cluster construction process and their inclusion relationships is provided to the end-user. This approach was developed and experimented in travel industry context with Amadeus SAS. Experiments show how it can provide a better segmentation, and refine the customer segments by identifying relevant sub-segments represented as sub-clusters in the hierarchical representation, for Customer Choice Modeling. The clustering of travelers was able to distinguish relevant segments of customers with similar needs and desires (i.e., customers purchasing tickets according to different criteria, like price, duration of flight, lay-over time, etc.) and at different levels of precision, which is a major issue for improving the personalization of recommendations in flight search queries.
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