The collaborative filtering (CF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Most prior CF methods adapted overall ratings to make predictions by collecting preference information from other users. However, in real applications, people’s preferences usually vary with time; the traditional CF could not properly reveal the change in users’ interests. In this paper, we propose a novel CF-based recommendation, dynamic decay collaborative filtering (DDCF), which captures the preference variations of users and includes the concept of dynamic time decay. We extend the idea of human brain memory to specify the level of a user’s interests (i.e., instantaneous, short-term, or long-term). According to different interest levels, DDCF dynamically tunes the decay function based on users’ behaviors. The experimental results show that DDCF with the integration of the dynamic decay concept performs better than traditional CF. In addition, we conduct experiments on real-world datasets to demonstrate the practicability of the proposed DDCF.

中文翻译：

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一种动态时间衰减的协同过滤推荐系统

由于对用户兴趣的精确预测，协同过滤（CF）技术在推荐系统中得到了广泛的应用。大多数先前的 CF 方法通过收集其他用户的偏好信息来调整整体评级以进行预测。然而，在实际应用中，人们的喜好通常会随着时间而变化；传统的CF不能很好地揭示用户兴趣的变化。在本文中，我们提出了一种新颖的基于 CF 的推荐，即动态衰减协同过滤（DDCF），它捕捉用户的偏好变化并包含动态时间衰减的概念。我们扩展了人类大脑记忆的概念，以指定用户的兴趣水平（即瞬时、短期或长期）。根据不同的兴趣水平，DDCF 根据用户的行为动态调整衰减函数。实验结果表明，结合动态衰减概念的DDCF比传统的CF性能更好。此外，我们对真实世界的数据集进行了实验，以证明所提出的 DDCF 的实用性。