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Incentivizing long-term engagement under limited budget

Wu, S and Bai, Q ORCID: 0000-0003-1214-6317 2019 , 'Incentivizing long-term engagement under limited budget', in AC Nayak and A Sharma (eds.), PRICAI 2019: Trends in Artificial Intelligence , Springer Nature, Switzerland, pp. 662-674 , doi: 10.1007/978-3-030-29908-8_52.

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Abstract

In recent years, more and more systems have been designed to affect users’ decisions for realizing certain system goals. However, most of these systems only focus on affecting users’ short-term or one-off behaviors, while ignoring the maintenance of users’ long-term engagement. In this light, we intend to design a novel approach which focuses on incentivizing users’ long-term engagement. In this paper, inspired by the use of Markov Decision Process (MDP), we first formally model the process of a user’s decision-making under long-term incentives. Subsequently, we propose the MDP-based Incentive Estimation (MDP-IE) approach for determining the value of an incentive and the requirement of obtaining that incentive. Experimental results demonstrate that the proposed approach can effectively sustain users’ long-term engagement. Furthermore, the experiments also demonstrate that incentivizing users’ long-term engagement is more beneficial than one-off or short-term approaches.

Item Type: Conference Publication
Authors/Creators:Wu, S and Bai, Q
Keywords: multi-agent systems, agent-based modelling, social network analysis, Markov Decision Process, incentive allocation
Journal or Publication Title: PRICAI 2019: Trends in Artificial Intelligence
Publisher: Springer Nature
ISSN: 0302-9743
DOI / ID Number: 10.1007/978-3-030-29908-8_52
Copyright Information:

Copyright 2019 Springer Nature Switzerland AG

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