Method and device for virtual resource allocation, modeling, and data prediction
View Patent ↗Evaluation results of a plurality of users are received from a plurality of data providers. The evaluation results are obtained by the plurality of data providers evaluating the plurality of users based on evaluation models of the plurality of data providers. A plurality of training samples is constructed by using the evaluation results. Each training sample includes a respective subset of the evaluation results corresponding to a same user of the plurality of users. A label for each training sample is generated based on an actual service execution status of the same user. A model is trained based on the plurality of training samples and the plurality of labels, including setting a plurality of variable coefficients, each variable coefficient specifying a contribution level of a corresponding data provider. Virtual resources to each data provider are allocated based on the plurality of variable coefficients.
1. A computer-implemented method, comprising:
receiving, from a plurality of data providers, evaluation results of a plurality of users, wherein the evaluation results are obtained by the plurality of data providers evaluating the plurality of users based on evaluation models of the plurality of data providers;
constructing a plurality of training samples by using the evaluation results uploaded by the plurality of data providers as training data, wherein each training sample comprises a respective subset of the evaluation results corresponding to a same user of the plurality of users;
generating a label for each training sample based on an actual service execution status of the same user to provide a plurality of labels;
training a model based on the plurality of training samples and the plurality of labels, wherein training the model comprises setting a plurality of variable coefficients, each variable coefficient specifying a contribution level of a corresponding data provider;
allocating virtual resources to each data provider based on the plurality of variable coefficients; and
receiving evaluation results of a particular user that are uploaded by the plurality of data providers, and inputting the evaluation results of the particular user to the trained model to obtain a final evaluation result of the particular user.
2. The computer-implemented method of claim 1 , wherein the trained model is a linear model.
3. The computer-implemented method of claim 1 , wherein a number of the virtual resources allocated to the each data provider is directly proportional to the plurality of variable coefficients.
4. The computer-implemented method of claim 3 , wherein the virtual resources comprise a user data usage fund distributed to the each data provider.
5. The computer-implemented method of claim 1 , wherein the evaluation models comprise a user risk evaluation model, the evaluation results comprise risk scores, and the label indicates whether the respective user is a risky user.
6. The computer-implemented method of claim 1 , wherein the trained model comprises a machine learning model.
7. The computer-implemented method of claim 6 , wherein the machine learning model comprises a logistic regression model.
8. The computer-implemented method of claim 1 , further comprising: generating a data feature vector for each training sample based on data feature values extracted based on dimensions of a respective training sample; and
constructing a target matrix based on the data feature vector of the each data sample.
9. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:
receiving, from a plurality of data providers, evaluation results of a plurality of users, wherein the evaluation results are obtained by the plurality of data providers evaluating the plurality of users based on evaluation models of the plurality of data providers;
constructing a plurality of training samples by using the evaluation results uploaded by the plurality of data providers as training data, wherein each training sample comprises a respective subset of the evaluation results corresponding to a same user of the plurality of users;
generating a label for each training sample based on an actual service execution status of the same user to provide a plurality of labels;
training a model based on the plurality of training samples and the plurality of labels, wherein training the model comprises setting a plurality of variable coefficients, each variable coefficient specifying a contribution level of a corresponding data provider;
allocating virtual resources to each data provider based on the plurality of variable coefficients; and
receiving evaluation results of a particular user that are uploaded by the plurality of data providers, and inputting the evaluation results of the particular user to the trained model to obtain a final evaluation result of the particular user.
10. The non-transitory, computer-readable medium of claim 9 , wherein the trained model is a linear model.
11. The non-transitory, computer-readable medium of claim 9 , wherein a number of the virtual resources allocated to the each data provider is directly proportional to the plurality of variable coefficients.
12. The non-transitory, computer-readable medium of claim 11 , wherein the virtual resources comprise a user data usage fund distributed to the each data provider.
13. The non-transitory, computer-readable medium of claim 9 , wherein the evaluation models comprise a user risk evaluation model, the evaluation results comprise risk scores, and the label indicates whether the respective user is a risky user.
14. The non-transitory, computer-readable medium of claim 9 , wherein the trained model comprises a machine learning model.
15. The non-transitory, computer-readable medium of claim 14 , wherein the machine learning model comprises a logistic regression model.
16. The non-transitory, computer-readable medium of claim 9 , wherein the operations further comprise:
generating a data feature vector for each training sample based on data feature values extracted based on dimensions of a respective training sample; and
constructing a target matrix based on the data feature vector of the each data sample.
17. A computer-implemented system,
comprising: one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform operations comprising:
receiving, from a plurality of data providers, evaluation results of a plurality of users, wherein the evaluation results are obtained by the plurality of data providers evaluating the plurality of users based on evaluation models of the plurality of data providers;
constructing a plurality of training samples by using the evaluation results uploaded by the plurality of data providers as training data, wherein each training sample comprises a respective subset of the evaluation results corresponding to a same user of the plurality of users;
generating a label for each training sample based on an actual service execution status of the same user to provide a plurality of labels;
training a model based on the plurality of training samples and the plurality of labels, wherein training the model comprises setting a plurality of variable coefficients, each variable coefficient specifying a contribution level of a corresponding data provider;
allocating virtual resources to each data provider based on the plurality of variable coefficients; and
receiving evaluation results of a particular user that are uploaded by the plurality of data providers, and inputting the evaluation results of the particular user to the trained model to obtain a final evaluation result of the particular user.
18. The computer-implemented system of claim 17 , wherein the trained model is a linear model.
19. The computer-implemented system of claim 17 , wherein a number of the virtual resources allocated to the each data provider is directly proportional to the plurality of variable coefficients.
20. The computer-implemented system of claim 19 , wherein the virtual resources comprise a user data usage fund distributed to the each data provider.
21. The computer-implemented system of claim 17 , wherein the evaluation models comprise a user risk evaluation model, the evaluation results comprise risk scores, and the label indicates whether the respective user is a risky user.
22. The computer-implemented system of claim 17 , wherein the trained model comprises a machine learning model.
23. The computer-implemented system of claim 22 , wherein the machine learning model comprises a logistic regression model.
24. The computer-implemented system of claim 17 , wherein the operations further comprise:
generating a data feature vector for each training sample based on data feature values extracted based on dimensions of a respective training sample; and
constructing a target matrix based on the data feature vector of the each data sample.