Abstract

With the development of the Internet and the progress of human-centered computing (HCC), the mode of man-machine collaborative work has become more and more popular. Valuable information in the Internet, such as user behavior and social labels, is often provided by users. A recommendation based on trust is an important human-computer interaction recommendation application in a social network. However, previous studies generally assume that the trust value between users is static, unable to respond to the dynamic changes of user trust and preferences in a timely manner. In fact, after receiving the recommendation, there is a difference between actual evaluation and expected evaluation which is correlated with trust value. Based on the dynamics of trust and the changing process of trust between users, this paper proposes a trust boost method through reinforcement learning. Recursive least squares (RLS) algorithm is used to learn the dynamic impact of evaluation difference on user’s trust. In addition, a reinforcement learning method Deep Q-Learning (DQN) is studied to simulate the process of learning user’s preferences and boosting trust value. Experiments indicate that our method applied to recommendation systems could respond to the changes quickly on user’s preferences. Compared with other methods, our method has better accuracy on recommendation.

中文翻译：

---

个性化项目建议：使用强化学习

抽象的

随着互联网的发展和以人为中心的计算技术的发展，人机协作的工作方式越来越流行。互联网上的宝贵信息（例如用户行为和社交标签）通常由用户提供。基于信任的推荐是社交网络中重要的人机交互推荐应用程序。但是，以前的研究通常假定用户之间的信任值是静态的，无法及时响应用户信任和偏好的动态变化。实际上，在收到推荐之后，实际评估与预期评估之间存在差异，该差异与信任值相关。根据信任的动态和用户之间信任的变化过程，本文提出了一种通过强化学习的信任提升方法。递归最小二乘（RLS）算法用于了解评估差异对用户信任度的动态影响。此外，还研究了一种强化学习方法“深度Q学习”（DQN），以模拟学习用户偏好并提高信任度的过程。实验表明，我们的方法应用于推荐系统可以根据用户的喜好快速响应变化。与其他方法相比，我们的方法在推荐上具有更好的准确性。实验表明，我们的方法应用于推荐系统可以快速响应用户的偏好更改。与其他方法相比，我们的方法在推荐上具有更好的准确性。实验表明，我们的方法应用于推荐系统可以快速响应用户的偏好更改。与其他方法相比，我们的方法在推荐上具有更好的准确性。