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.