In recent years, mining user multi-behavior information for prediction has become a hot topic in recommendation systems. Usually, researchers only use graph networks to capture the relationship between multiple types of user-interaction information and target items, while ignoring the order of interactions. This makes multi-behavior information underutilized. In response to the above problem, we propose a new hybrid graph network recommendation model called the User Multi-Behavior Graph Network (UMBGN). The model uses a joint learning mechanism to integrate user–item multi-behavior interaction sequences. We designed a user multi-behavior information-aware layer to focus on the long-term multi-behavior features of users and learn temporally ordered user–item interaction information through BiGRU units and AUGRU units. Furthermore, we also defined the propagation weights between the user–item interaction graph and the item–item relationship graph according to user behavior preferences to capture more valuable dependencies. Extensive experiments on three public datasets, namely MovieLens, Yelp2018, and Online Mall, show that our model outperforms the best baselines by 2.04%, 3.82%, and 3.23%.

Multimodality has been widely used for sentiment analysis tasks, especially for speech sentiment analysis. Compared with the emotion expression of most text languages, speech is more intuitive for human emotion, as speech contains more and richer emotion features. Most of the current studies mainly involve the extraction of speech features, but the accuracy and prediction rate of the models still need to be improved. To improve the extraction and fusion of speech sentiment feature information, we present a new framework. The framework adopts a hierarchical conformer model and an attention-based GRU model to increase the accuracy of the model. The method has two main parts: a local feature learning group and a global feature learning group. The local feature learning group is mainly used to learn the spatio-temporal feature information of speech emotion features through the conformer model, and a combination of convolution and transformer is used to be able to enhance the extraction of long and short-term feature information. The global features are then extracted by the AUGRU model, and the fusion of features is performed by the attention mechanism to access the weights of feature information. Finally, the sentiment is identified by a fully connected network layer, and then classified by a central loss function and a softmax function. Compared with existing speech sentiment analysis models, we obtained better sentiment classification results on the IEMOCAP and RAVDESS benchmark datasets.