GAC：一种面向未知社交网络用户激励的深度强化学习模型
GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks
摘要：
近年来，许多应用程序都部署了激励机制来促进用户的注意力和参与度。大多数激励机制基于用户的属性（例如，偏好），而这样的信息在许多现实世界的应用中是不可用的。同时，由于预算限制，实现对所有用户的成功激励可能具有挑战性。有鉴于此，我们考虑利用社会影响力，以最大限度地提高激励效果。我们可以直接激励有影响力的用户来影响更多的用户，这样就可以降低激励这些用户的成本。然而，识别社交网络中有影响力的用户需要关于用户之间的影响力强度的完整信息，这在现实世界的情况下是不切实际的。在这项研究中，我们提出了一个端到端的强化学习为基础的框架，称为几何演员批评（GAC），以解决上述问题。该方法不需要用户属性的先验知识就能实现有效的激励分配。实验中采用了三个真实的社交网络数据集来评估GAC的性能。实验结果表明，GAC能够在未知的社交网络中学习和应用有效的激励分配策略，并优于现有的激励分配方法。
英文摘要：
In recent years, many applications have deployed incentive mechanisms to promote users' attention and engagement. Most incentive mechanisms determine specific incentive values based on users' attributes (e.g., preferences), while such information is unavailable in many real-world applications. Meanwhile, due to budget restrictions, realizing successful incentivization for all users can be challenging to complete. In this light, we consider leveraging social influence to maximize the incentivization result. We can directly incentivize influential users to affect more users, so the cost of incentivizing these users can be decreased. However, identifying influential users in a social network requires complete information about influence strength among users, which is impractical to acquire in real-world situations. In this research, we propose an end-to-end reinforcement learning-based framework, called Geometric Actor-Critic (GAC), to tackle the abovementioned problem. The proposed approach can realize effective incentive allocation without having prior knowledge about users' attributes. Three real-world social network datasets have been adopted in the experiments to evaluate the performance of GAC. The experimental results indicate that GAC can learn and apply effective incentive allocation policies in unknown social networks and outperform existing incentive allocation approaches.
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