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：用于移动物联网的基于深度学习的联合云视频推荐框架

在物联网（IoT）时代，在移动设备上观看视频已成为我们日常生活中的一种流行应用。如何向用户推荐视频是Internet视频服务提供商（IVSP）最为关注的问题之一。为了向用户提供更好的推荐服务，他们以地理位置分布的方式部署了云服务器。每个服务器负责分析用户数据的本地区域。因此，这些云服务器形成了信息孤岛，并且数据的特征呈现出非独立且相同的分布（非iid）。在这种情况下，很难为每个区域中的少数用户提供准确的视频推荐服务。为了解决这个问题，我们提出了JointRec，这是一个基于深度学习的联合云视频推荐框架。JointRec将JointCloud体系结构集成到移动物联网中，并在分布式云服务器之间实现联合培训。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。基于此模型，每个云都可以通过利用用户的个人资料和用户评分的视频描述来推荐视频，从而提供更准确的视频推荐服务。然后，我们提出了一种联合推荐算法，该算法可使每个云共享其权重并协作训练模型。此外，考虑到联合训练过程中沉重的通信成本，我们将低秩矩阵分解和8位量化方法相结合，以减少上行链路通信成本和网络带宽。