Customer Interaction Networks based on Multiple Instance Similarities

Ivett Elena Fuentes Herrera; Gonzalo Nápoles; Leticia Arco; Koen Vanhoof

doi:10.1007/978-3-030-53337-3_21

Customer Interaction Networks based on Multiple Instance Similarities

Ivett Elena Fuentes Herrera, Gonzalo Nápoles, Leticia Arco, Koen Vanhoof

Informatica en Toegepaste Informatica

Onderzoeksoutput: Chapter › peer review

Samenvatting

Understanding customer behaviors is deemed crucial to improve customers' satisfaction and loyalty, which eventually is materialized in increased revenue. This paper tackles this challenge by using complex networks and multiple instance reasoning to examine the network structure of Customer Purchasing Behaviors. Our main contributions rely on a new multiple instance similarity to measure the interaction among customers based on the mutual information theory focuses on the customers' bags, a new network construction approach involving customers, orders and products, and a new measure for evaluating its internal consistency. The simulations using 12 real-world problems support the eectiveness of our proposal.

Originele taal-2	English
Titel	Business Information Systems
Subtitel	23rd International Conference on Business Information Systems
Redacteuren	Witold Abramowicz, Gary Klein
Uitgeverij	Springer Verlag
Pagina's	279-290
Aantal pagina's	12
Volume	389
ISBN van elektronische versie	978-3-030-53337-3
ISBN van geprinte versie	978-3-030-53336-6
DOI's	https://doi.org/10.1007/978-3-030-53337-3_21
Status	Published - 22 jul 2020
Evenement	International Conference on Business Information Systems - University of Colorado, Colorado Springs, United States Duur: 8 jun 2020 → 10 jun 2020 Congresnummer: 23 https://bisconf.org/2020/

Publicatie series

Naam	Lecture Notes in Business Information Processing
Uitgeverij	Springer

Conference

Conference	International Conference on Business Information Systems
Verkorte titel	BIS
Land/Regio	United States
Stad	Colorado Springs
Periode	8/06/20 → 10/06/20
Internet adres	https://bisconf.org/2020/

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10.1007/978-3-030-53337-3_21

Customer Interaction Networks based on Multiple Instance Similarities
Ivett Fuentes (Speaker), Gonzalo Nápoles (Speaker), Leticia Arco Garcia (Speaker) & Koen Vanhoof (Speaker)
8 jun 2020 → 10 jun 2020
Activiteit: Talk or presentation at a conference

Citeer dit

Fuentes Herrera, I. E., Nápoles, G., Arco, L., & Vanhoof, K. (2020). Customer Interaction Networks based on Multiple Instance Similarities. In W. Abramowicz, & G. Klein (editors), Business Information Systems: 23rd International Conference on Business Information Systems (Vol. 389, blz. 279-290). (Lecture Notes in Business Information Processing). Springer Verlag. https://doi.org/10.1007/978-3-030-53337-3_21

Fuentes Herrera, Ivett Elena ; Nápoles, Gonzalo ; Arco, Leticia ; Vanhoof, Koen. / Customer Interaction Networks based on Multiple Instance Similarities. Business Information Systems: 23rd International Conference on Business Information Systems. redacteur / Witold Abramowicz ; Gary Klein. Vol. 389 Springer Verlag, 2020. blz. 279-290 (Lecture Notes in Business Information Processing).

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title = "Customer Interaction Networks based on Multiple Instance Similarities",

abstract = "Understanding customer behaviors is deemed crucial to improve customers' satisfaction and loyalty, which eventually is materialized in increased revenue. This paper tackles this challenge by using complex networks and multiple instance reasoning to examine the network structure of Customer Purchasing Behaviors. Our main contributions rely on a new multiple instance similarity to measure the interaction among customers based on the mutual information theory focuses on the customers' bags, a new network construction approach involving customers, orders and products, and a new measure for evaluating its internal consistency. The simulations using 12 real-world problems support the eectiveness of our proposal.",

keywords = "customer networks, complex network construction, multiple instance similarities, Customer Purchasing Behaviors, Community detection",

author = "{Fuentes Herrera}, {Ivett Elena} and Gonzalo N{\'a}poles and Leticia Arco and Koen Vanhoof",

year = "2020",

month = jul,

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doi = "10.1007/978-3-030-53337-3_21",

language = "English",

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editor = "Witold Abramowicz and Gary Klein",

booktitle = "Business Information Systems",

address = "Germany",

note = "International Conference on Business Information Systems, BIS ; Conference date: 08-06-2020 Through 10-06-2020",

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Fuentes Herrera, IE, Nápoles, G, Arco, L & Vanhoof, K 2020, Customer Interaction Networks based on Multiple Instance Similarities. in W Abramowicz & G Klein (redactie), Business Information Systems: 23rd International Conference on Business Information Systems. vol. 389, Lecture Notes in Business Information Processing, Springer Verlag, blz. 279-290, International Conference on Business Information Systems, Colorado Springs, United States, 8/06/20. https://doi.org/10.1007/978-3-030-53337-3_21

Customer Interaction Networks based on Multiple Instance Similarities. / Fuentes Herrera, Ivett Elena; Nápoles, Gonzalo; Arco, Leticia; Vanhoof, Koen.

Business Information Systems: 23rd International Conference on Business Information Systems. redactie / Witold Abramowicz; Gary Klein. Vol. 389 Springer Verlag, 2020. blz. 279-290 (Lecture Notes in Business Information Processing).

Onderzoeksoutput: Chapter › peer review

TY - CHAP

T1 - Customer Interaction Networks based on Multiple Instance Similarities

AU - Fuentes Herrera, Ivett Elena

AU - Nápoles, Gonzalo

AU - Arco, Leticia

AU - Vanhoof, Koen

N1 - Conference code: 23

PY - 2020/7/22

Y1 - 2020/7/22

N2 - Understanding customer behaviors is deemed crucial to improve customers' satisfaction and loyalty, which eventually is materialized in increased revenue. This paper tackles this challenge by using complex networks and multiple instance reasoning to examine the network structure of Customer Purchasing Behaviors. Our main contributions rely on a new multiple instance similarity to measure the interaction among customers based on the mutual information theory focuses on the customers' bags, a new network construction approach involving customers, orders and products, and a new measure for evaluating its internal consistency. The simulations using 12 real-world problems support the eectiveness of our proposal.

AB - Understanding customer behaviors is deemed crucial to improve customers' satisfaction and loyalty, which eventually is materialized in increased revenue. This paper tackles this challenge by using complex networks and multiple instance reasoning to examine the network structure of Customer Purchasing Behaviors. Our main contributions rely on a new multiple instance similarity to measure the interaction among customers based on the mutual information theory focuses on the customers' bags, a new network construction approach involving customers, orders and products, and a new measure for evaluating its internal consistency. The simulations using 12 real-world problems support the eectiveness of our proposal.

KW - customer networks

KW - complex network construction

KW - multiple instance similarities

KW - Customer Purchasing Behaviors

KW - Community detection

U2 - 10.1007/978-3-030-53337-3_21

DO - 10.1007/978-3-030-53337-3_21

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Fuentes Herrera IE, Nápoles G, Arco L, Vanhoof K. Customer Interaction Networks based on Multiple Instance Similarities. In Abramowicz W, Klein G, redacteurs, Business Information Systems: 23rd International Conference on Business Information Systems. Vol. 389. Springer Verlag. 2020. blz. 279-290. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-030-53337-3_21

Customer Interaction Networks based on Multiple Instance Similarities

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Customer Interaction Networks based on Multiple Instance Similarities

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