Open Access Repository
Incentivizing long-term engagement under limited budget

Full text not available from this repository.
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 |
Item Statistics: | View statistics for this item |
Actions (login required)
![]() |
Item Control Page |