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

Informatics and Applied Informatics

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

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.

Original language	English
Title of host publication	Business Information Systems
Subtitle of host publication	23rd International Conference on Business Information Systems
Editors	Witold Abramowicz, Gary Klein
Publisher	Springer Verlag
Pages	279-290
Number of pages	12
Volume	389
ISBN (Electronic)	978-3-030-53337-3
ISBN (Print)	978-3-030-53336-6
DOIs	https://doi.org/10.1007/978-3-030-53337-3_21
Publication status	Published - 22 Jul 2020
Event	International Conference on Business Information Systems - University of Colorado, Colorado Springs, United States Duration: 8 Jun 2020 → 10 Jun 2020 Conference number: 23 https://bisconf.org/2020/

Publication series

Name	Lecture Notes in Business Information Processing
Publisher	Springer

Conference

Conference	International Conference on Business Information Systems
Abbreviated title	BIS
Country/Territory	United States
City	Colorado Springs
Period	8/06/20 → 10/06/20
Internet address	https://bisconf.org/2020/

Keywords

customer networks
complex network construction
multiple instance similarities
Customer Purchasing Behaviors
Community detection

Access to Document

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

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Persistent link

Customer Interaction Networks based on Multiple Instance Similarities
Fuentes, I. (Speaker), Nápoles, G. (Speaker), Arco Garcia, L. (Speaker) & Vanhoof, K. (Speaker)
8 Jun 2020 → 10 Jun 2020
Activity: Talk or presentation › Talk or presentation at a conference

Cite this

Fuentes Herrera, I. E., Nápoles, G., Arco, L., & Vanhoof, K. (2020). Customer Interaction Networks based on Multiple Instance Similarities. In W. Abramowicz, & G. Klein (Eds.), Business Information Systems: 23rd International Conference on Business Information Systems (Vol. 389, pp. 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 et al. / Customer Interaction Networks based on Multiple Instance Similarities. Business Information Systems: 23rd International Conference on Business Information Systems. editor / Witold Abramowicz ; Gary Klein. Vol. 389 Springer Verlag, 2020. pp. 279-290 (Lecture Notes in Business Information Processing).

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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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Fuentes Herrera, IE, Nápoles, G, Arco, L & Vanhoof, K 2020, Customer Interaction Networks based on Multiple Instance Similarities. in W Abramowicz & G Klein (eds), Business Information Systems: 23rd International Conference on Business Information Systems. vol. 389, Lecture Notes in Business Information Processing, Springer Verlag, pp. 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 et al.
Business Information Systems: 23rd International Conference on Business Information Systems. ed. / Witold Abramowicz; Gary Klein. Vol. 389 Springer Verlag, 2020. p. 279-290 (Lecture Notes in Business Information Processing).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

TY - CHAP

T1 - Customer Interaction Networks based on Multiple Instance Similarities

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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

M3 - Chapter

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VL - 389

T3 - Lecture Notes in Business Information Processing

SP - 279

EP - 290

BT - Business Information Systems

A2 - Abramowicz, Witold

A2 - Klein, Gary

PB - Springer Verlag

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Y2 - 8 June 2020 through 10 June 2020

ER -

Fuentes Herrera IE, Nápoles G, Arco L, Vanhoof K. Customer Interaction Networks based on Multiple Instance Similarities. In Abramowicz W, Klein G, editors, Business Information Systems: 23rd International Conference on Business Information Systems. Vol. 389. Springer Verlag. 2020. p. 279-290. (Lecture Notes in Business Information Processing). doi: 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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