Vehicle edge computing (VEC) is a new technology that can extend computing and storage functions to the edge of the Internet of Things (IoT) systems. For limited computing power and delay sensitive mobile applications on the Internet of Vehicles (IOV). It is important to offload computing tasks to the end of the VEC network. Still, High mobility data security and privacy resource management and the randomness of IOV brought about new problems to the offloading of VEC. To this end, this study focuses on the offloading of computing tasks in VEC. We survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different categories. We also discuss the prospect of VEC. This survey could give a reference for researchers to find and understand the important characteristics of VEC, which helps choose the optimal solutions for the offloading of computing tasks in VEC.

Social recommendation can effectively alleviate the problems of data sparseness and the cold start of recommendation systems, attracting widespread attention from researchers and industry. Current social recommendation models use social relations to alleviate the problem of data sparsity and improve recommendation performance. Although self-supervised learning based on user–item interaction can enhance the performance of such models, multi-auxiliary information is neglected in the learning process. Therefore, we propose a model based on self-supervision and multi-auxiliary information using multi-auxiliary information, such as user social relationships and item association relationships, to make recommendations. Specifically, the user social relationship and item association relationship are combined to form a multi-auxiliary information graph. The user–item interaction relationship is also integrated into the same heterogeneous graph so that multiple pieces of information can be spread in the same graph. In addition, we utilize the graph convolution method to learn user and item embeddings, whereby the user embeddings reflect both user–item interaction and user social relationships, and the item embeddings reflect user–item interaction and item association relationships. We also design multi-view self-supervising auxiliary tasks based on the constructed multi-auxiliary views. Signals generated by self-supervised auxiliary tasks can alleviate the problem of data sparsity, further improving user/item embedding quality and recommendation performance. Extensive experiments on two public datasets verify the superiority of the proposed model.

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