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Adaptive incentive allocation for influence-aware proactive recommendation

Citation

Wu, S and Bai, Q and Kang, B, Adaptive incentive allocation for influence-aware proactive recommendation, PRICAI 2019: Trends in Artificial Intelligence, 26-30 August 2019, Cuvu, Fiji, pp. 649-661. ISSN 0302-9743 (2019) [Refereed Conference Paper]

Copyright Statement

Copyright 2019 Springer Nature Switzerland AG

DOI: doi:10.1007/978-3-030-29908-8_51

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 Details

Item Type:Refereed Conference Paper
Keywords:multi-agent systems, agent-based modelling, social network analysis, incentives allocation, proactive recommendation, unknown network
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Bai, Q (Dr Quan Bai)
UTAS Author:Kang, B (Professor Byeong Kang)
ID Code:138236
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-05-21
Downloads:0

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