预算约束下未知社交网络中影响力用户识别与自适应激励分配
Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction
摘要：
近年来，推荐系统在许多领域得到了广泛的应用。这些系统在影响用户选择系统期望的行为方面是无能为力的。与此同时，提供激励已被证明是一种更积极主动的方式来影响用户的行为。由于预算限制，可以激励的用户数量受到限制。有鉴于此，我们打算利用用户之间存在的社会影响力来提高激励效果。通过直接激励有影响力的用户，他们在社交网络中的追随者可能被间接激励。然而，在许多现实场景中，网络的拓扑结构通常是未知的，这使得识别有影响力的用户变得困难。为了解决上述问题，本文提出了一种新的算法，在未知的网络中发现有影响力的用户，它可以估计用户之间的影响力关系的基础上，他们的历史行为，而不知道网络的拓扑结构。同时，我们设计了一种自适应的激励分配方法，根据用户的偏好和他们的影响力来确定激励值。我们通过在合成和真实世界的数据集上进行实验来评估所提出的方法的性能。实验结果证明了所提方法的有效性。
英文摘要：
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 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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
