Information diffusion prediction aims to predict the next infected user in the information diffusion, which is a critical task to understand how information spreads on social platforms. Existing methods mainly focus on the sequences or topology structure in euclidean space. However, they fail to sufficiently consider the hierarchical structure or power-law structure of the underlying topology of information cascade graphs and social networks, resulting in distortion of user features. To tackle above issue, we propose an innovative Constrained Temporal Hypergraphs and Graph Neural Networks (THGNets) framework that is tailored for information diffusion prediction. Specifically, we introduce hyperbolic temporal hypergraphs neural network to alleviate the distortion of user features by hyperbolic hierarchical learning in information cascades. Additionally, it also captures high-order dynamic interaction patterns between users and further integrates the time-consistency constraint mechanism to mitigate the instability and non-smoothness of user features in latent space. In parallel, we apply the hyperbolic graph neural network to investigate the hierarchical structure and user homogeneity on social networks, enhancing our understanding of social relationships. Moreover, hyperbolic gated recurrent units are employed to capture the potential dependency relationships between contextual users. Experiments conducted on four public datasets demonstrate that the proposed THGNets significantly outperform the existing methods, thereby validating the superiority and rationality of our approach.

Multimodal sentiment analysis (MSA) is a considerable research area within the domain of social networks where users continuously share a vast amount of content. This content includes text, images, and audio, all of which are accompanied with sentiment or emotion expressions. MSA encompasses multiple tasks, such as sentiment prediction and emotion recognition, which have multi-task dynamic relationships owing to their close correlation and complementary nature. However, previous works using a single-task framework have been limited in exploring these dynamics. In this work, we propose a discriminative joint multi-task framework (DJMF) realizing sentiment prediction and emotion recognition synchronously. The discriminative learning strategy takes into account intra-task dynamics, and customized fusion schemes are applied to the subtasks. Furthermore, we utilize a joint training strategy to capture the inter-task dynamics by leveraging their interrelatedness, enhancing the performance of each task. Additionally, in the fusion process, we incorporate two categories of regularizers to enable the model to obtain distinct fusion results by reducing redundancy and task-irrelevant information. We conducted extensive evaluations on three well-known multimodal baseline datasets: CMU-MOSI, CMU-MOSEI, and UR-FUNNY. The results demonstrate that our approach offers better reliability and stability compared to the single-task framework and outperforms state-of-the-art methods.

Nowadays, with the massive demand for sports news, automatic generation systems based on the template technology has been deployed, which could generate massive sports news quickly and effectively. However, by using one simple template for one scenario, the pattern of text generated by such system is single. In this paper, we propose an automatic generation method based on knowledge rules to select the template dynamically from a template set. The text generated by the system is flexible and the format is varied, which improves the quality of the generated news.

Demand-side platform is the key process for real-time bidding of online advertising. The appropriate design of entity relationship of Demand-side platform has become the primary problem to launch demand. In order to solve the problem of online advertising in Demand-side platform has been the lack of a clear definition of the problem of entity relationship, a system design has been proposed to combines the online advertising real-time bidding specific demands and advertisers demands to meet the online advertising business requirements. The design not only defines the module division of Demand-side platform system, but also puts forward core entity relationship. Furthermore, it also provides a good foundation for further online advertising with efficient, flexible ways.