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Caching Policy for Cache-Enabled D2D Communications by Learning User Preference

机译：通过学习用户首选项来启用缓存的D2D通信的缓存策略

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摘要

Prior works in a designing caching policy do not distinguish content popularity with user preference. In this paper, we illustrate the caching gain by exploiting individual user behavior in sending requests. After showing the connection between the two concepts, we provide a model for synthesizing user preference from content popularity. We then optimize the caching policy with the knowledge of user preference and activity level to maximize the offloading probability for cache-enabled device-to-device communications, and develop a low-complexity algorithm to find the solution. In order to learn user preference, we model the user request behavior resorting to probabilistic latent semantic analysis, and learn the model parameters by the expectation maximization algorithm. By analyzing a Movielens data set, we find that the user preferences are less similar, and the activity level and topic preference of each user change slowly over time. Based on this observation, we introduce a prior knowledge-based learning algorithm for user preference, which can shorten the learning time. Simulation results show a remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic data set and synthetic data validated by the real data set.
机译：设计缓存策略中的现有技术无法区分内容受欢迎程度和用户偏好。在本文中，我们通过在发送请求时利用单个用户的行为来说明缓存的获得。在展示了这两个概念之间的联系之后，我们提供了一个用于根据内容受欢迎程度综合用户偏好的模型。然后，我们利用用户偏好和活动级别的知识来优化缓存策略，以最大程度地提高启用缓存的设备到设备通信的卸载概率，并开发一种低复杂度的算法来找到解决方案。为了学习用户偏好，我们利用概率潜在语义分析对用户请求行为进行建模，并通过期望最大化算法学习模型参数。通过分析Movielens数据集，我们发现用户首选项不太相似，并且每个用户的活动级别和主题首选项随时间缓慢变化。基于此观察，我们针对用户偏好引入了一种基于知识的现有学习算法，该算法可以缩短学习时间。仿真结果表明，缓存策略在用户偏爱具有内容流行度的现有策略方面具有显着的性能提升，无论是现实数据集还是通过真实数据集验证的合成数据，都具有优势。

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