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

Abstract

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Event attendance classification in social media

“…Events in social media have been extensively studied. The main aspects investigated in the litera- Foursquare [25,26,27,28,29]; (2) recommendation of events to users [5,30,31]; 3estimation of the number of attendees in a given event [32]; and (4) modeling participants' behavior during an event. [33,4,34] Du et al [25] analyzed an EBSN to predict users' attendance by taking into account the content, the spatial and temporal context, the users' preferences and their social influence.…”

“…Their experiments suggested that the use of network-based features is very important since it allows to increase the AUC from 0.22% to 0.82%. The approach presented in [29] aims to predict the response to event invitations in the Meetic social network. The authors proposed and evaluated a competing risk methodology for the task showing how their method performs better than the baselines.…”

Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda

“…In the US, data protection is partly regulated by the Privacy Act of 1974, which establishes a code of fair practice to govern the collection of personal data, the Health Insurance Portability and Accountability Act of 1996 (HIPAA) to protect health information privacy rights, and the Electronic Communications Privacy Act (ECPA) of 1986 that establishes sanctions for interception of electronic communication. As a response, research in OR mainly focuses on the input side (Li, 2018) to protect data, but other aspects could be considered as well. Hence, more research is still needed on the development of privacy-preserving solutions.…”

“…The General Data Protection Regulation (GDPR; implemented May 25, 2018) also establishes that every individual consumer has the right to receive an explanation of any decision made by an algorithm, as well as the right to privacy. Li (2018), seeking to build an online invitation response prediction model, proposes a novel, privacy-friendly mixture cure model with Bayesian networks. The predictive accuracy improves by 24% but still accounts for privacy considerations in relation to the input data.…”