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Predicting online invitation responses with a competing risk model using privacy-friendly social event data

Predicting online invitation responses with a competing risk model using privacy-friendly social event data
Predicting online invitation responses with a competing risk model using privacy-friendly social event data
Predicting people's responses to invitations is an important issue for social event management, as the decision-making process behind member responses to invitations is complicated. The purpose of this paper is to suggest a privacy-friendly method to predict whether and when people will respond to open invitations. We apply the competing risk model to predict member responses. The predictive model uses past social event participation data to infer a network structure among people who accept or reject invitations. The inferred networks collectively show the extent to which people are likely to accept or reject invitations. Validated using real datasets including 31,230 people and 8,885 events, the proposed method not only presents the variables that predict attendance (such as past attendance and social network), but also those that predict faster responses. This approach is privacy friendly, as it requires no personal information regarding people and social events (such as name, age and gender or event content). This work contributes to the predictive modeling literature as the first study of a competing risk model developed for replies to a social invitation. Our findings will help event organizers predict how many people will attend events, allowing them to organize effectively.
0377-2217
698-708
Li, Libo
838dda30-da62-41ad-b57c-bee6ad59acd3
Li, Libo
838dda30-da62-41ad-b57c-bee6ad59acd3

Li, Libo (2018) Predicting online invitation responses with a competing risk model using privacy-friendly social event data. European Journal of Operational Research, 270 (2), 698-708. (doi:10.1016/j.ejor.2018.03.036).

Record type: Article

Abstract

Predicting people's responses to invitations is an important issue for social event management, as the decision-making process behind member responses to invitations is complicated. The purpose of this paper is to suggest a privacy-friendly method to predict whether and when people will respond to open invitations. We apply the competing risk model to predict member responses. The predictive model uses past social event participation data to infer a network structure among people who accept or reject invitations. The inferred networks collectively show the extent to which people are likely to accept or reject invitations. Validated using real datasets including 31,230 people and 8,885 events, the proposed method not only presents the variables that predict attendance (such as past attendance and social network), but also those that predict faster responses. This approach is privacy friendly, as it requires no personal information regarding people and social events (such as name, age and gender or event content). This work contributes to the predictive modeling literature as the first study of a competing risk model developed for replies to a social invitation. Our findings will help event organizers predict how many people will attend events, allowing them to organize effectively.

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More information

Accepted/In Press date: 27 March 2018
e-pub ahead of print date: 5 April 2018
Published date: 16 October 2018

Identifiers

Local EPrints ID: 436657
URI: http://eprints.soton.ac.uk/id/eprint/436657
ISSN: 0377-2217
PURE UUID: f8378143-c5a7-48df-a591-fc8709dce2e8
ORCID for Libo Li: ORCID iD orcid.org/0000-0003-1658-5157

Catalogue record

Date deposited: 20 Dec 2019 17:51
Last modified: 28 Apr 2022 02:29

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