Embedded deep representation of social network taxonomy

• US 10,459,901 B2
• Filed: 11/23/2016
• Issued: 10/29/2019
• Est. Priority Date: 11/23/2016
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Merhav, Uri, Jing, How, Yang, Jaewon, Shacham, Dan
• Primary Examiner(s): Beausoliel, Jr., Robert W
• Assistant Examiner(s): Bhuyan, Mohammad Solaiman
• Application Number:
  US15/360,547
• Publication Number:
  US 20180144008A1
• Time in Patent Office: 1,070 Days
• CPC Class Codes: G06F 16/217 Database tuning G06F16/2282 takes precedence; database performance monitoring G06F11/3409
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

In an example embodiment, for each of a plurality of different entities in a social network structure, the entity is mapped into a vector having n coordinates. The vector for each of the plurality of different entities is stored in a deep representation data structure. One or more objective functions are applied to at least one combination of two or more of the vectors in the deep representation data structure. Then, an optimization test on each of the at least one combination of two or more of the vectors is performed using a corresponding objective function output for each of the at least one combination of two or more of the vectors, and, for any combination that did not pass the optimization test, one or more coordinates for the vectors in the combination are altered so that the vectors in the combination become closer together within an n-dimensional space.

Claims

1. A system comprising:

a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to;

for each of a plurality of different entities in a social network structure, wherein each entity is a different attribute for users in a social network, map the entity into a vector having n coordinates based on an embedding, wherein the embedding initially defines that each value stored in the vector for each of the n coordinates is selected randomly from a preset range, wherein each entity is a standardized value for data in the social network structure;

store the vector for each of the plurality of different entities in a deep representation data structure;

apply one or more objective functions to at least one combination of two or more of the vectors in the deep representation data structure, causing an objective function output for each of the at least one combination of two or more of the vectors;

perform an optimization test on each of the at least one combination of two or more of the vectors using a corresponding objective function output for each of the at least one combination of two or more of the vectors;

determine, for each of the at least one combination of two or more of the vectors, whether the combination passed the optimization test; and

for any combination that did not pass the optimization test, alter the embedding by changing one or more values coordinates in the vectors in the combination so that the vectors in the combination become closer together within an n-dimensional space, and repeat the performing of the optimization test on the altered vectors, altering the vectors again continually until the optimization test is passed.

2. The system of claim 1, wherein the different entities are entities of a different entity type.

3. The system of claim 1, wherein the preset range is between 0 and 1.

4. The system of claim 1, wherein the optimization test includes, for each combination, examining data in the social network structure to determine whether a first entity corresponding to a first vector in the combination co-occurs in the data with a second entity corresponding to a second vector in the combination.

5. The system of claim 1, wherein at least one of the objective functions is a dot product.

6. The system of claim 1, wherein the optimization test includes feeding a piece of data containing a first entity corresponding to a first vector in the combination and a second entity corresponding to a second vector in the combination to a machine learning model trained to, from entities of a first entity type of the first entity, predict the presence of entities of a second entity type of the second entity, to determine whether the machine learning model accurately predicts the presence of the second entity in the piece of data.

7. The system of claim 6, wherein the piece of data is a member profile.

8. The system of claim 1, wherein the instructions further comprise using distance between the vectors within the n-dimensional space as an indicator of whether a user has a skill represented in the social network structure.

9. A computer-implemented method, comprising:

for each of a plurality of different entities in a social network structure, wherein each entity is a different attribute for users in a social network, mapping the entity into a vector having n coordinates based on an embedding, wherein the embedding initially defines that each value stored in the vector for each of the n coordinates is selected randomly from a preset range, wherein each entity is a standardized value for data in the social network structure;

storing the vector for each of the plurality of different entities in a deep representation data structure;

applying one or more objective functions to at least one combination of two or more of the vectors in the deep representation data structure, causing an objective function output for each of the at least one combination of two or more of the vectors;

performing an optimization test on each of the at least one combination of two or more of the vectors using a corresponding objective function output for each of the at least one combination of two or more of the vectors;

determining, for each of the at least one combination of two or more of the vectors, whether the combination passed the optimization test; and

for any combination that did not pass the optimization test, altering the embedding by changing one or more values coordinates in the vectors in the combination so that the vectors in the combination become closer together within an n-dimensional space, and repeating the performing of the optimization test on the altered vectors, altering the vectors again continually until the optimization test is passed.

10. The method of claim 9, wherein the different entities are entities of a different entity type.

11. The method of claim 9, wherein the preset range is between 0 and 1.

12. The method of claim 9, wherein the optimization test includes, for each combination, examining data in the social network structure to determine whether a first entity corresponding to a first vector in the combination co-occurs in the data with a second entity corresponding to a second vector in the combination.

13. The method of claim 9, wherein at least one of the objective functions is a dot product.

14. The method of claim 9, wherein the optimization test includes feeding a piece of data containing a first entity corresponding to a first vector in the combination and a second entity corresponding to a second vector in the combination to a machine learning model trained to, from entities of a first entity type of the first entity, predict the presence of entities of a second entity type of the second entity, to determine whether the machine learning model accurately predicts the presence of the second entity in the piece of data.

15. The method of claim 14, wherein the piece of data is a member profile.

16. A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:

for each of a plurality of different entities in a social network structure, wherein each entity is a different attribute for users in a social network, mapping the entity into a vector having n coordinates based on an embedding, wherein the embedding initially defines that each value stored in the vector for each of the n coordinates is selected randomly from a preset range, wherein each entity is a standardized value for data in the social network structure;

storing the vector for each of the plurality of different entities in a deep representation data structure;

applying one or more objective functions to at least one combination of two or more of the vectors in the deep representation data structure, causing an objective function output for each of the at least one combination of two or more of the vectors;

performing an optimization test on each of the at least one combination of two or more of the vectors using a corresponding objective function output for each of the at least one combination of two or more of the vectors;

determining, for each of the at least one combination of two or more of the vectors, whether the combination passed the optimization test; and

for any combination that did not pass the optimization test, altering the embedding by changing one or more values coordinates in the vectors in the combination so that the vectors in the combination become closer together within an n-dimensional space, and repeating the performing of the optimization test on the altered vectors, altering the vectors again continually until the optimization test is passed.

17. The non-transitory machine-readable storage medium of claim 16, wherein the different entities are entities of a different entity type.

18. The non-transitory machine-readable storage medium of claim 16, wherein the preset range is between 0 and 1.

19. The non-transitory machine-readable storage medium of claim 16, wherein the optimization test includes, for each combination, examining data in the social network structure to determine whether a first entity corresponding to a first vector in the combination co-occurs in the data with a second entity corresponding to a second vector in the combination.

20. The non-transitory machine-readable storage medium of claim 16, wherein the optimization test includes feeding a piece of data containing a first entity corresponding to a first vector in the combination and a second entity corresponding to a second vector in the combination to a machine learning model trained to, from entities of a first entity type of the first entity, predict the presence of entities of a second entity type of the second entity, to determine whether the machine learning model accurately predicts the presence of the second entity in the piece of data.