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Predicting Online Invitation Responses with a Competing Risk Model Using Privacy-Friendly Social Event Data

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). pp. 698-708. ISSN 0377-2217. (doi:10.1016/j.ejor.2018.03.036) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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http://dx.doi.org/10.1016/j.ejor.2018.03.036

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.

Item Type: Article
DOI/Identification number: 10.1016/j.ejor.2018.03.036
Uncontrolled keywords: Decision support systems; Social network analysis; Survival analysis; Predictive modeling
Subjects: H Social Sciences
Divisions: Faculties > Social Sciences > Kent Business School > Marketing
Depositing User: Libo Li
Date Deposited: 12 Dec 2018 11:40 UTC
Last Modified: 14 Jan 2020 16:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/70958 (The current URI for this page, for reference purposes)
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