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2019 | OriginalPaper | Buchkapitel

Adaptive Incentive Allocation for Influence-Aware Proactive Recommendation

verfasst von : Shiqing Wu, Quan Bai, Byeong Ho Kang

Erschienen in: PRICAI 2019: Trends in Artificial Intelligence

Verlag: Springer International Publishing

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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.

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Titel: Adaptive Incentive Allocation for Influence-Aware Proactive Recommendation
verfasst von: Shiqing Wu
Quan Bai
Byeong Ho Kang
Verlag: Springer International Publishing
Buch: PRICAI 2019: Trends in Artificial Intelligence
Print ISBN: 978-3-030-29907-1

Electronic ISBN: 978-3-030-29908-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-29908-8_51

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