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Incentivizing long-term engagement under limited budget
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 IntelligenceEditors
AC Nayak and A SharmaPagination
662-674ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
Springer NaturePlace of publication
SwitzerlandEvent title
16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019)Event Venue
Cuvu, FijiDate of Event (Start Date)
2019-08-26Date of Event (End Date)
2019-08-30Rights statement
Copyright 2019 Springer Nature Switzerland AGRepository Status
- Restricted