Yemin Sun's research while affiliated with Guilin University of Electronic Technology and other places

The prediction of the popularity of online content, particularly videos, has recently gained significant attention as successful popularity prediction can assist many practical applications such as recommendation systems and proactive caching, as well as aid in optimizing advertising strategies or balancing network throughput. Despite much work being done on predicting the popularity of online videos, there are still challenges to be overcome: (1) popularity is greatly influenced by various external factors, resulting in significant fluctuations that are difficult to capture and track; (2) online video content and metadata information are typically diverse, sparse, and noisy, making the prediction task complex and unstable; (3) some data have temporal relevance, and the impact on popularity varies at different times. In this paper, we propose an Adaptive Temporal Knowledge Graph Network (ATKN) video popularity prediction model to address the issues surrounding video popularity prediction. First, we employ the attention-based Long Short-Term Memory (ALSTM) network to capture the trend of popularity change. Then, we introduce an Attention-based Factorization Machine (AFM) with attention mechanism to model the feature cross of video content, thereby enhancing the distinction of importance after different feature crosses. Next, we use a Relational Graph Convolutional Network (RGCN) to extract the associated features between entities in the knowledge graph. Finally, we propose a dynamic feature fusion method that adaptively assigns the weights of temporal features and content features at different time intervals by constructing an exponential decay function, thereby obtaining an effective and stable feature fusion module. Experimental results demonstrate the superiority and interpretability of ATKN on the MovieLens-20M dataset and the Microsoft Satori-built movie knowledge graph.

With the advancement of cloud computing technology, many service providers are combining with cloud service providers to build a highly available streaming video-on-demand cloud platform and provide video services to end users. Generally, cloud service providers deploy many edge cloud CDN nodes in different geographic areas and provide video services to end users. However, when an end-user wants to watch certain videos and request video resources from surrounding edge cloud CDN nodes, the edge cloud CDN node will request missing video clips from other cloud nodes. Therefore, this will generate a large amount of additional video transmission costs and reduce the quality of service of the cloud service provider. To reduce or even minimize the video transmission cost of edge cloud CDN nodes while ensuring the quality of service (QoS). We designed a video transmission algorithm called Netdmc to ensure transmission quality. The algorithm can be divided into two parts. The first part is a low-latency video request algorithm based on ensuring service quality, and the second part is a video request algorithm based on minimizing video transmission costs. The simulation results demonstrate that the Netdmc algorithm can effectively reduce the cost of cloud service providers and ensure the quality of video services. KeywordsCloud service providerVideo transmission costQoS