In the era of Internet of Things (IoT), watching videos on mobile devices has been a popular application in our daily life. How to recommend videos to users is one of the most concerned problem for Internet video service providers (IVSPs). In order to provide better recommendation service to users, they deploy cloud servers in a geo-distributed manner. Each server is responsible for analyzing a local area of user data. Therefore, these cloud servers form information islands and the characteristics of data present nonindependent and identically distribution (non-i.i.d). In this scenario, it is difficult to provide accurate video recommendation service to the minority of users in each area. To tackle this issue, we propose JointRec, a deep learning-based joint cloud video recommendation framework. JointRec integrates the JointCloud architecture into mobile IoT and achieves federated training among distributed cloud servers. Specifically, we first design a dual-convolutional probabilistic matrix factorization (Dual-CPMF) model to conduct video recommendation. Based on this model, each cloud can recommend videos by exploiting the user's profiles and description of videos that users rate, thereby providing more accurate video recommendation services. Then, we present a federated recommendation algorithm which enables each cloud to share their weights and train a model cooperatively. Furthermore, considering the heavy communication costs in the process of federated training, we combine low-rank matrix factorization and 8-bit quantization method to reduce uplink communication costs and network bandwidth. We validate the proposed approach on the real-world data set, and the experimental results indicate the effectiveness of our proposed approach.

中文翻译：

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JointRec：基于深度学习的移动物联网联合云视频推荐框架


在物联网（IoT）时代，通过移动设备观看视频已成为我们日常生活中的流行应用。如何向用户推荐视频是互联网视频服务提供商（IVSP）最关心的问题之一。为了给用户提供更好的推荐服务，他们以地理分布式的方式部署云服务器。每个服务器负责分析本地区域的用户数据。因此，这些云服务器形成了信息孤岛，数据呈现非独立同分布（non-iid）的特征。在这种场景下，很难为各个地区的少数用户提供精准的视频推荐服务。为了解决这个问题，我们提出了JointRec，一个基于深度学习的联合云视频推荐框架。 JointRec将JointCloud架构融入到移动物联网中，实现分布式云服务器之间的联邦训练。具体来说，我们首先设计一个双卷积概率矩阵分解（Dual-CPMF）模型来进行视频推荐。基于该模型，各云可以利用用户的个人资料和用户评分的视频描述来推荐视频，从而提供更精准的视频推荐服务。然后，我们提出了一种联合推荐算法，该算法使每个云能够共享其权重并协作训练模型。此外，考虑到联邦训练过程中通信成本较高，我们结合低秩矩阵分解和8位量化方法来降低上行通信成本和网络带宽。我们在现实世界的数据集上验证了所提出的方法，实验结果表明了我们所提出的方法的有效性。