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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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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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