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.
History
Publication title
PRICAI 2019: Trends in Artificial Intelligence
Editors
AC Nayak and A Sharma
Pagination
662-674
ISSN
0302-9743
Department/School
School of Information and Communication Technology
Publisher
Springer Nature
Place of publication
Switzerland
Event title
16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019)
Event Venue
Cuvu, Fiji
Date of Event (Start Date)
2019-08-26
Date of Event (End Date)
2019-08-30
Rights statement
Copyright 2019 Springer Nature Switzerland AG
Repository Status
Restricted
Socio-economic Objectives
Information systems, technologies and services not elsewhere classified