With the surge of academic papers, it has become a common practice to recommend papers based on authors’ research interests. Existing methods focus on leveraging author–paper research interactions to mine authors’ research interests with coauthorship networks. However, sparse research interactions would pose a huge challenge to distinguish research interests of authors. Fortunately, inter-dependent knowledge across papers provides rich potential heterogeneous connections for author–paper interactions, offering much insights for learning authors’ research interests. Therefore, we propose a meta-relation–guided knowledge coupling approach for paper recommendation. Specifically, we construct a meta-relation–guided heterogeneous graph architecture to depict the numerous inter-dependencies among authors and papers, thereby exploring complex author–paper interactions. First, a meta-relation–aware heterogeneous graph encoder is developed to extract relational structure which maintains the relation-specific representation of authors’ research interest and papers’ research relatedness. Then, a cross-meta-path attention network is designed to aggregate the characteristics of different meta-relations and obtain research features of authors and papers. Finally, a self-supervised data augmentation architecture is constructed to mine and preserve local and global graph structure information, acquiring papers with high relevance to author’s research interests through training loss. Numerous experiments are conducted on two real academic datasets, effectively demonstrating the superiority of our proposed model and validating its effectiveness in paper recommendation.

This paper concerns a Virtual Reality(VR) video stream processing based on edge-cloud collaboration. Since VR video streaming demands high resolution and frame rates, current VR terminals often struggle to achieve optimal decoding speeds. This paper proposes a video stream processing algorithm that leverages edge cloud collaboration, combining the strong computing power and large delay of cloud servers with the weak computing power and fast response of edge servers. According to the current communication situation and the computing power distribution situation of the server, the video decoding task is split and allocated to the cloud server and the edge server respectively. The algorithm utilizes the different features of both servers to minimize latency. In the process of task splitting, it is necessary to allocate computing resources to different tasks of edge servers with different computing power. In this paper, the problem of computing resource allocation is modeled as a constrained problem and transformed into an unconstrained problem, which is then optimized by applying the Sequential Least SQuares Programming optimization algorithm(SLSQP). The simulation results demonstrate that the proposed algorithm outperforms existing methods in reducing video transmission time.