Self-supervised graph neural network with pre-training generative learning for recommendation systems

The case assignment system is an essential system of case management and assignment within the procuratorate and is an important aspect of judicial fairness and efficiency. However, existing methods mostly use manual or random case assignment, which leads to unbalanced case distribution. Moreover, the relationship between prosecutors and case categories usually shows a power-law distribution in real-world data. Therefore, in this paper, we describe the case rationality assignment as a recommendation problem under the power-law distributed data. To solve the above problems, we propose an end-to-end S elf-supervised G raph neural network model with P re-training G enerative learning for Rec ommendation (SGPGRec), the main idea of which is to capture self-supervised signals using intra-node features and inter-node correlations in the data, and generate the data representation by pre-training to improve the recommendation results. To be specific, we designed three auxiliary self-supervised tasks based on the prosecutor-case category interaction graph and the data distribution to obtain feature representations of prosecutors, case categories, and the interaction information between them. Then we constructed an end-to-end graph neural network recommendation model by the interaction information based on the data characteristics of the power-law distribution. Finally, extensive experimental consistency on a real-world dataset from three procuratorates shows that our method is effective compared to several yet competing baseline methods and further supports the development of an intelligent case assignment system with adequate performance.