Most recommendation systems are designed for seeking users’ demands and preferences, whereas impotent to affect users’ decisions for realizing the system-level objective. In this light, we intend to propose a generic concept named ‘proactive recommendation’, which focuses on not only maintaining users’ satisfaction but also realizing system-level objectives. In this paper, we claim the proactive recommendation is crucial for the scenario where the system objectives are required to realize. To realize proactive recommendation, we intend to affect users’ decision-making by providing incentives and utilizing social influence between users. We design an approach for discovering the influential users in an unknown network, and a dynamic game-based mechanism that allocates incentives to users dynamically. The preliminary experimental results show the effectiveness of the proposed approach.
History
Publication title
PRICAI 2019: Trends in Artificial Intelligence
Editors
AC Nayak and A Sharma
Pagination
649-661
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