A method and system are provided. The method includes receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data. The method further includes generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer. The recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: receiving by a computer having a processor and a memory, sequence data that includes labeled data and unlabeled data; and generating, by the computer having the processor and the memory, a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer, wherein the recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data. 2. The method of claim 1, wherein the recurrent neural network model is generated to form a semi-supervised recurrent neural network model having the recurrent layer and the aggregate layer by applying a graph-based supervised learning process to the unlabeled data. 3. The method of claim 2, wherein the graph-based supervised learning process is applied to the unlabeled data in the aggregation layer of the semi-supervised recurrent neural network model. 4. The method of claim 3, wherein the aggregation layer in the aggregation layer of the semi-supervised recurrent neural network model and the graph-based supervised learning process provide aggregation of data having different domains. 5. The method of claim 2, further comprising generating a prediction using the semi-supervised recurrent neural network model, wherein the prediction is generated based on a joint utilization of the labeled data and the unlabeled data. 6. The method of claim 2, wherein the graph-based supervised learning process is applied to the labeled data and the unlabeled data to extract similarity data there between, and wherein the semi-supervised recurrent neural network model is formed using the similarity data. 7. The method of claim 1, wherein the recurrent neural network model is generated to form a deep recurrent neural network, and wherein the labeled data and the unlabeled data are jointly trained by the deep recurrent neural network. 8. The method of claim 1, wherein the generated sequences from the recurrent layer correspond to different domains that are regularized in the aggregate layer based on graph data. 9. The method of claim 1, further comprising constructing one or more graphs to describe a similarity between data samples in the sequence data as an input to the recurrent neural network model. 10. The method of claim 9, wherein the aggregate layer performs data aggregation based on the similarity between the data samples. 11. The method of claim 1, further comprising generating one or more customer behavior predictions using the recurrent neural network. 12. The method of claim 11, wherein at least one of the customer behavior predictions comprises at least one response rate prediction. 13. A non-transitory article of manufacture tangibly embodying a computer readable program which when executed causes a computer to perform the steps of claim 1. 14. A system, comprising: a computer, having a processor and a memory, configured to: receive sequence data that includes labeled data and unlabeled data; and generate a recurrent neural network model of the sequence data, the recurrent neural network model having a recurrent layer and an aggregate layer, wherein the recurrent neural network model feeds sequences generated from the recurrent layer into the aggregate layer for aggregation, stores temporal dependencies in the sequence data, and generates labels for at least some of the unlabeled data. 15. The system of claim 14, wherein the computer is implemented as a server using a cloud computing configuration. 16. The system of claim 14, wherein the computer generates the recurrent neural network model to form a semi-supervised recurrent neural network model having the recurrent layer and the aggregate layer by applying a graph-based supervised learning process to the unlabeled data. 17. The system of claim 16, wherein the graph-based supervised learning process is applied to the unlabeled data in the aggregation layer of the semi-supervised recurrent neural network model. 18. The system of claim 16, further comprising generating a prediction using the semi-supervised recurrent neural network model, wherein the prediction is generated based on a joint utilization of the labeled data and the unlabeled data. 19. The system of claim 16, wherein the graph-based supervised learning process is applied to the labeled data and the unlabeled data to extract similarity data there between, and wherein the semi-supervised recurrent neural network model is formed using the similarity data. 20. The system of claim 14, wherein the recurrent neural network model is generated to form a deep recurrent neural network, and wherein the labeled data and the unlabeled data are jointly trained by the deep recurrent neural network.