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Incentivizing long-term engagement under limited budget

conference contribution
posted on 2023-05-23, 14:32 authored by Wu, S, Quan BaiQuan Bai
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

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