Joint embedding of corpus pairs for domain mapping

1. A system, comprising:

a memory that stores computer executable components; and

a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise:

a computation component that generates using a trained model an embedded representation of a first keyword associated with a first domain corpus and generates a second embedded representation of second keywords associated with a second domain corpus via the trained model; and

a scoring component that determines an affinity value based on comparing the first keyword to the second keywords, and based on the affinity value, aggregates a joint embedding of the first embedded representation of the first keyword and the second embedded representation of the second keywords within the second domain corpus.

2. The system of claim 1 , wherein the second keywords comprise a plurality of user identities associated with a plurality of user profiles.

3. The system of claim 1 , wherein the affinity value is generated in response to an unsupervised estimation, and wherein the unsupervised estimation comprises a cluster analysis of a plurality of first keywords and the second keywords.

4. The system of claim 1 , wherein a plurality of potential first keywords comprise a second keyword associated with a plurality of web pages.

5. The system of claim 1 , wherein the affinity value is related to a scoring of the joint embedding of the first embedded representation and the second embedded representation.

6. The system of claim 5 , wherein the scoring of the joint embedding affinity comprises, based on satisfaction of a defined condition associated with a frequency of the first keyword, outputting a keyword collection ranking, resulting in a ranked first keyword, to the second domain corpus.

7. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable to:

generate a first embedded representation of a first keyword associated with a first domain corpus via a trained model and generate a second embedded representation of second keywords associated with a second domain corpus via the trained model; and

determine an affinity value based on comparing the first keyword to the second keywords; and based on the affinity value, aggregating a joint embedding of the first embedded representation of the first keyword and the second embedded representation of the second keywords within the second domain corpus.

8. The computer program product of claim 7 , wherein the affinity value is related to a scoring of the joint embedding of the first embedded representation and the second embedded representation.

9. The computer program product of claim 8 , wherein the program instructions are further executable to:

based on satisfaction of a defined condition associated with a frequency of the first keyword, output a keyword collection ranking, resulting in a ranked first keyword, to the second domain corpus.

10. The computer program product of claim 9 , wherein the keyword collection ranking is ranked in descending order.

11. The computer program product of claim 9 , wherein the keyword collection ranking comprises taxonomy tag data associated with a taxonomy tag.

12. The computer program product of claim 9 , wherein the program instructions are further executable to:

output the keyword collection ranking to the first domain corpus.

13. The computer program product of claim 7 , wherein the plurality of second keywords comprises a plurality of user identities associated with a plurality of user profiles.

14. The computer program product of claim 7 , wherein the affinity value is generated in response to an unsupervised estimation, and wherein the unsupervised estimation comprises a cluster analysis of a plurality of first keywords and the plurality of second keywords.

15. The computer program product of claim 7 , wherein a plurality of first potential keywords comprise a second keyword associated with a plurality of web pages.

16. A computer-implemented method, comprising:

generating, by a device operatively coupled to a processor, a first embedded representation of a first keyword associated with a first domain corpus via a trained model and generating a second embedded representation of second keywords associated with a second domain corpus via the trained model; and

determining an affinity value based on comparing the first keyword to the second keywords, and based on the affinity value, aggregating the joint embedding of the first embedded representation of the first keyword and the second embedded representation of the second keywords within the second domain corpus.

17. The computer-implemented method of claim 16 , wherein the affinity value is related to a scoring of the joint embedding of the first embedded representation and the second embedded representation.

18. The computer-implemented method of claim 17 , further comprising:

based on satisfaction of a defined condition associated with a frequency of the first keyword, outputting, by the device, a keyword collection ranking, resulting in a ranked first keyword, to the second domain corpus.

19. The computer-implemented method of claim 16 , wherein a keyword collection ranking comprises taxonomy tag data associated with a taxonomy tag.

20. The computer-implemented method of claim 16 , further comprising:

generating, by the device, a stochastic gradient descent value related to the trained model.