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Adaptive incentive allocation for influence-aware proactive recommendation
conference contribution
posted on 2023-05-23, 14:32 authored by Wu, S, Quan BaiQuan Bai, Byeong KangByeong KangMost 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 IntelligenceEditors
AC Nayak and A SharmaPagination
649-661ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
Springer NaturePlace of publication
SwitzerlandEvent title
16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019)Event Venue
Cuvu, FijiDate of Event (Start Date)
2019-08-26Date of Event (End Date)
2019-08-30Rights statement
Copyright 2019 Springer Nature Switzerland AGRepository Status
- Restricted