The complex structure and diverse imformation of stock markets make stock movement prediction extremely challenging. However， most of the existing studies treat each stock as an individual or use graph structures to model complex higher-order relationships in stock markets， without considering the hierarchy and dynamics among stocks， industries and markets. Aiming at the above problems， a Dynamic Macro Memory Network （DMMN） was proposed， and price movement prediction was performed for multiple stocks simultaneously based on DMMN. In this method， the market macro-environmental information was modeled by the hierarchies of “stock-industry-market”， and long-term dependences of this information on time series were captured. Then， the market macro-environmental information was integrated with stock micro-characteristic information dynamically to enhance the ability of each stock to perceive the overall state of the market and capture the interdependences among stocks， industries， and markets indirectly. Experimental results on the collected CSI300 dataset show that compared with stock prediction methods based on Attentive Long Short-Term Memory （ALSTM） network， GCN-LSTM （Graph Convolutional Network with Long Short-Term Memory）， Convolutional Neural Network （CNN） and other models， the DMMN-based method achieves better results in F1-score and Sharpe ratio， which are improved by 4.87% and 31.90% respectively compared with ALSTM， the best model among all comparison methods. This indicates that DMMN has better prediction performance and better practicability.

Mainstream personalized recommendation systems usually use models deployed in the cloud to perform recommendation， so the private data such as user interaction behaviors need to be uploaded to the cloud， which may cause potential risks of user privacy leakage. In order to protect user privacy， user-sensitive data can be processed on the client， however， there are communication bottleneck and computation resource bottleneck in clients. Aiming at the above challenges， a personalized recommendation service system based on cloud-client-convergence was proposed. In this system， the cloud-based recommendation model was divided into a user representation model and a sorting model. After being pre-trained on the cloud， the user representation model was deployed to the client， while the sorting model was deployed to the cloud. A small-scale Recurrent Neural Network （RNN） was used to model the user behavior characteristics by extracting temporal information from user interaction logs， and the Lasso （Least absolute shrinkage and selection operator） algorithm was used to compress user representations， thereby preventing a drop in recommendation accuracy while reducing the communication overhead between the cloud and the client as well as the computation overhead of the client. Experiments were conducted on RecSys Challenge 2015 dataset， and the results show that the recommendation accuracy of the proposed system is comparable to that of the GRU4REC model， while the volume of the compressed user representations is only 34.8% of that before compression， with a lower computational overhead.