arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1707.08409cs.ITmath.IT
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通过学习用户偏好的启用缓存的D2D通信的缓存策略

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

Binqiang Chen, Chenyang Yang

摘要:

在设计缓存策略中的先前工作不区分内容流行度与用户偏好。在本文中,我们说明了利用个人用户的行为在发送请求的缓存增益。在展示了这两个概念之间的联系之后,我们提供了一个从内容流行度合成用户偏好的模型。然后,我们优化的缓存策略与知识的用户偏好和活动水平,以最大限度地提高卸载概率的缓存启用设备到设备的通信,并开发一个低复杂度的算法来找到解决方案。为了学习用户偏好,我们采用概率潜在语义分析对用户请求行为进行建模,并采用期望最大化算法学习模型参数。通过分析Movielens数据集,我们发现用户偏好不太相似,每个用户的活跃程度和主题偏好随着时间的推移而缓慢变化。在此基础上,提出了一种基于先验知识的用户偏好学习算法,该算法可以缩短学习时间。仿真结果表明,显着的性能增益的缓存策略与用户的偏好,现有的政策与内容流行度,无论是与现实的数据集和合成数据验证的真实数据集。

英文摘要:

Prior works in 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 active 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 expectation maximization algorithm. By analyzing a Movielens dataset, we find that the user preferences are less similar, and the active 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 remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic dataset and synthetic data validated by the real dataset.

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。

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