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Abstract

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.

Item Type:	Conference Publication
Authors/Creators:	Wu, S and Bai, Q and Kang, B
Keywords:	multi-agent systems, agent-based modelling, social network analysis, incentives allocation, proactive recommendation, unknown network
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_51
Copyright Information:	Copyright 2019 Springer Nature Switzerland AG
Related URLs:	Google Scholar: Kang, B UTAS Profile: Kang, B
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