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Incentivizing long-term engagement under limited budget

Citation

Wu, S and Bai, Q, Incentivizing long-term engagement under limited budget, PRICAI 2019: Trends in Artificial Intelligence, 26-30 August 2019, Cuvu, Fiji, pp. 662-674. ISSN 0302-9743 (2019) [Refereed Conference Paper]

Copyright Statement

Copyright 2019 Springer Nature Switzerland AG

DOI: doi:10.1007/978-3-030-29908-8_52

Abstract

In recent years, more and more systems have been designed to affect usersí decisions for realizing certain system goals. However, most of these systems only focus on affecting usersí short-term or one-off behaviors, while ignoring the maintenance of usersí long-term engagement. In this light, we intend to design a novel approach which focuses on incentivizing usersí long-term engagement. In this paper, inspired by the use of Markov Decision Process (MDP), we first formally model the process of a userís 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 usersí long-term engagement. Furthermore, the experiments also demonstrate that incentivizing usersí long-term engagement is more beneficial than one-off or short-term approaches.

Item Details

Item Type:Refereed Conference Paper
Keywords:multi-agent systems, agent-based modelling, social network analysis, Markov Decision Process, incentive allocation
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138235
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-05-21
Downloads:0

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