Incentivizing Long-Term Engagement Under Limited Budget

Wu, Shiqing; Bai, Quan

doi:10.1007/978-3-030-29908-8_52

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Although these approaches can perform effectively in incentive allocation, their performance could not be maintained when the information they rely on is unavailable. Meanwhile, the counterfactual inference-based approaches, which generate incentives by learning the logged feedback data from users, are also widely studied [21,22]. A typical example is the budgeted multi-armed bandit problem, in which options are modeled as arms, and the target is to find out the most beneficial arm to select [23,24,25].…”

Section: Related Workmentioning

confidence: 99%

GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks

Wu¹,

Li²,

Bai³

2022

Preprint

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In recent years, providing incentives to human users for attracting their attention and engagement has been widely adopted in many applications. To effectively incentivize users, most incentive mechanisms determine incentive values based on users' individual attributes, such as preferences. These approaches could be ineffective when such information is unavailable. Meanwhile, due to the budget limitation, the number of users who can be incentivized is also restricted. In this light, we intend to utilize social influence among users to maximize the incentivization. By directly incentivizing influential users in the social network, their followers and friends could be indirectly incentivized with fewer incentives or no incentive. However, it is difficult to identify influential users beforehand in the social network, as the influence strength between each pair of users is typically unknown. In this work, we propose an end-to-end reinforcement learning-based framework, named Geometric Actor-Critic (GAC), to discover effective incentive allocation policies under limited budgets. More specifically, the proposed approach can extract information from a high-level network representation for learning effective incentive allocation policies. The proposed GAC only requires the topology of the social network and does not rely on any prior information about users' attributes. We use three real-world social network datasets to evaluate the performance of the proposed GAC. The experimental results demonstrate the effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks

Wu¹,

Li²,

Bai³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given a budget restriction, the goal of incentive allocation is to maximize the effect of users incentivization [49]. To better realize such a goal, some incentive allocation approaches are designed and tuned according to the features of scenarios, where the allocation policy determines incentive values based on specific attributes, such as users' preferences, location, and skill abilities [12,47,24,34]. These approaches assume such necessary attributes are acquirable and utilize these attributes to model users' demands to incentives, such that effective incentive allocation can be realized.…”

Section: Incentive Allocationmentioning

confidence: 99%

“…Many incentive allocation approaches have been developed [37,54]. However, most existing approaches determine incentive values based on attributes of users only while ignoring the influence existing among them [12,34,47]. As shown in Figure 1, existing traditional incentive allocation may distribute incentives to all users based on its pricing policy, which is either customized or uniform.…”

Section: Problem Descriptionmentioning

confidence: 99%

Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction

Li²,

Shen³

et al. 2021

Preprint

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In recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more proactive way to affect users' behaviors. Due to the budget limitation, the number of users who can be incentivized is restricted. In this light, we intend to utilize social influence existing among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers or friends in the social network are possibly incentivized indirectly. However, in many real-world scenarios, the topological structure of the network is usually unknown, which makes identifying influential users difficult. To tackle the aforementioned challenges, in this paper, we propose a novel algorithm specifically for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network. Meanwhile, we design an adaptive incentive allocation approach that determines incentive values based on users' preferences and their influence ability. We evaluate the performance of the proposed approaches by conducting experiments on both synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.

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Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction

Shen

et al. 2023

Information Sciences

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Incentivizing Long-Term Engagement Under Limited Budget

Cited by 4 publications

References 14 publications

GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks

GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks

Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction

Identifying influential users in unknown social networks for adaptive incentive allocation under budget restriction

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