Identifying similarity matrix for derived perceptions

1. A computer-implemented method comprising:

initializing a budget value for iterative generation of a number of unique queries, the unique queries comprising respective unique query sets of objects;

generating a query prompting a user to select from among a plurality of response options related to a first query set of objects,

wherein the first query set of objects are from object data that is representative of a plurality of objects;

displaying the query to the user via a graphical user interface (GUI);

receiving, responsive to the query, user input via the GUI, the user input representative of a selected response option selected by the user from among the plurality of response options;

calculating, responsive to the user input, a plurality of weight values via a reward strategy for respective ones of a plurality of similarity matrices based on the selected response option, wherein the reward strategy comprises increasing the weight values based on a number of similarity matrices that agree with the selected response option,

wherein the plurality of similarity matrices include respective different sets of similarity values, each set of similarity values comprising similarity values representative of similarities of respective pairs of the plurality of objects;

repeating the generating of the query for a number of iterations that does not exceed the budget value, each iteration comprising prompting the user with respective unique query sets of objects; and

storing, on a selected storage medium, a designated similarity matrix from among the plurality of similarity matrices, the designated similarity matrix being designated from among the plurality of similarity matrices based at least in part on a highest weight value from among the plurality of weight values assigned to the designated similarity matrix.

2. The computer-implemented method of claim 1 , further comprising:

receiving a request for a user-specific input for a machine learning algorithm, the request comprising at least a portion of the object data.

3. The computer-implemented method of claim 2 , wherein the request further comprises at least one of the plurality of similarity matrices.

4. The computer-implemented method of claim 2 , further comprising:

modifying input data for a machine-learning algorithm using the designated similarity matrix.

5. The computer-implemented method of claim 4 , wherein the machine-learning algorithm includes natural language processing that processing text using the designated similarity matrix.

6. The computer-implemented method of claim 1 , further comprising:

calculating, responsive to the user input, a plurality of reward values for respective ones of the plurality of similarity matrices based on the selected response option, wherein the reward values are based at least in part on the number of similarity matrices that agree with the selected response option.

7. The computer-implemented method of claim 1 , wherein the first query set of objects comprises a first object, a second object, and a third object, and

wherein the plurality of response options comprises:

a first response option indicating that the first object and the second object are more similar than the first object and the third object; and

a second response option indicating that the first object and the third object are more similar than the first object and the second object.

8. The computer-implemented method of claim 7 , wherein the plurality of response options further comprises a third response option indicating that the user is declining to provide the first response option and declining to provide the second response option.

9. A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising:

generating a query prompting a user to select from among a plurality of response options related to a first query set of objects,

wherein the first query set of objects are from object data that is representative of a plurality of objects;

displaying the query to the user via a graphical user interface (GUI);

receiving, responsive to the query, user input via the GUI, the user input representative of a selected response option selected by the user from among the plurality of response options;

calculating, responsive to the user input, a plurality of weight values via a reward strategy for respective ones of a plurality of similarity matrices based on the selected response option, wherein the reward strategy comprises increasing the weight values based on a number of similarity matrices that agree with the selected response option,

wherein the plurality of similarity matrices include respective different sets of similarity values, each set of similarity values comprising similarity values representative of similarities of respective pairs of the plurality of objects; and

storing, on a selected storage medium, a designated similarity matrix from among the plurality of similarity matrices, the designated similarity matrix being designated from among the plurality of similarity matrices based at least in part on a highest weight value from among the plurality of weight values assigned to the designated similarity matrix.

10. The computer program product of claim 9 , wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system.

11. The computer program product of claim 9 , wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising:

program instructions to meter use of the program instructions associated with the request; and

program instructions to generate an invoice based on the metered use.

12. The computer program product of claim 9 , further comprising:

modifying input data for a machine-learning algorithm using the designated similarity matrix.

13. The computer program product of claim 9 , wherein the first query set of objects comprises a first object, a second object, and a third object, and

wherein the plurality of response options comprises:

a first response option indicating that the first object and the second object are more similar than the first object and the third object; and

a second response option indicating that the first object and the third object are more similar than the first object and the second object.

14. The computer program product of claim 13 , wherein the plurality of response options further comprises a third response option indicating that the user is declining to provide the first response option and declining to provide the second response option.

15. A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations comprising:

generating a query prompting a user to select from among a plurality of response options related to a first query set of objects,

wherein the first query set of objects are from object data that is representative of a plurality of objects;

displaying the query to the user via a graphical user interface (GUI);

receiving, responsive to the query, user input via the GUI, the user input representative of a selected response option selected by the user from among the plurality of response options;

calculating, responsive to the user input, a plurality of weight values via a reward strategy for respective ones of a plurality of similarity matrices based on the selected response option, wherein the reward strategy comprises increasing the weight values based on a number of similarity matrices that agree with the selected response option,

wherein the plurality of similarity matrices include respective different sets of similarity values, each set of similarity values comprising similarity values representative of similarities of respective pairs of the plurality of objects; and

storing, on a selected storage medium, a designated similarity matrix from among the plurality of similarity matrices, the designated similarity matrix being designated from among the plurality of similarity matrices based at least in part on a highest weight value from among the plurality of weight values assigned to the designated similarity matrix.

16. The computer system of claim 15 , further comprising:

modifying input data for a machine-learning algorithm using the designated similarity matrix.

17. The computer system of claim 15 , wherein the first query set of objects comprises a first object, a second object, and a third object, and

wherein the plurality of response options comprises:

a first response option indicating that the first object and the second object are more similar than the first object and the third object; and

a second response option indicating that the first object and the third object are more similar than the first object and the second object.

18. The computer system of claim 17 , wherein the plurality of response options further comprises a third response option indicating that the user is declining to provide the first response option and declining to provide the second response option.