Artificial Intelligence Based Job Wages Benchmarks

1 . A computer-implemented method of predictive benchmarking, the method comprising:

collecting, by a number of processors, wage data from a number of sources, wherein the wage data comprises a number of dimensions;

preprocessing, by a number of processors, the wage data;

training, by a number of processors, a wide linear part of a wide-and-deep model to emulate benchmarks and to memorize exceptions and co-occurrence of dimensions in the wage data;

training, by a number of processors, a deep part of the wide-and-deep model to generalize rules for wage predictions across employment sectors based on relationships between dimensions, wherein the deep part is trained concurrently with the wide linear part;

receiving, by a number of processors, a user request for a number of wage benchmark forecasts;

forecasting, by a number of processors, a number of wage benchmarks, wherein linear coefficients produced by the wide linear part are summed with nonlinear coefficients produced by the deep part according to parameters in the user request; and

displaying, by a number of processors, the wage benchmark forecasts.

2 . The method of claim 1 , wherein wage benchmarks comprise at least one of:

average annual base salary;

median annual base salary;

percentiles of annual base salary;

average hourly rate;

median hourly rate; or

percentiles of hourly rate.

3 . The method of claim 2 , wherein the wide-and-deep model uses linear regression to calculate average base salary.

4 . The method of claim 2 , wherein the wide-and-deep model uses quartile regression to calculate percentile of base salary.

5 . The method of claim 1 , wherein the dimensions comprise at least one of:

region;

subregion;

work state;

metropolitan and micropolitan statistical area codes;

combined metropolitan statistical area codes;

North American Industry Classification System codes;

industry sector;

industry subsector;

industry supersector;

industry combo;

industry crosssector;

employee headcount band;

employer revenue band;

job title;

occupation;

job level; or

tenure.

6 . The method of claim 1 , wherein the wide-and-deep model is trained through transfer learning.

7 . The method of claim 1 , wherein the linear wide part of the model assists the deep part of the model with residual learning.

8 . The method of claim 1 , wherein cross terms provide sharing information between pairs of dimensions, and wherein dimensions are added to correct for the outliers in the wage data.

9 . The method of claim 1 , wherein dimension embeddings map benchmark dimensions to lower-dimensional vectors, wherein categories predefined as similar to each other have values within a predefined proximity at one or more coordinates.

10 . A system for predictive benchmarking, the system comprising:

a bus system;

a storage device connected to the bus system, wherein the storage device stores program instructions; and

a number of processors connected to the bus system, wherein the number of processors execute the program instructions to:

collect wage data from a number of sources, wherein the wage data comprises a number of dimensions;

preprocess the wage data;

train a wide linear part of a wide-and-deep model to emulate benchmarks and to memorize exceptions and co-occurrence of dimensions in the wage data;

train a deep part of the wide-and-deep model to generalize rules for wage predictions across employment sectors based on relationships between dimensions, wherein the deep part is trained concurrently with the wide linear part;

receive a user request for a number of wage benchmark forecasts

forecast a number of wage benchmarks, wherein linear coefficients produced by the wide linear part are summed with nonlinear coefficients produced by the deep part according to parameters in the user request; and

display the wage benchmark forecasts.

11 . The system of claim 10 , wherein wage benchmarks comprise at least one of:

average annual base salary;

median annual base salary;

percentiles of annual base salary;

average hourly rate;

median hourly rate; or

percentiles of hourly rate.

12 . The system of claim 11 , wherein the wide-and-deep model uses linear regression to calculate average base salary.

13 . The system of claim 11 , wherein the wide-and-deep model uses quartile regression to calculate percentile of base salary.

14 . The system of claim 10 , wherein the dimensions comprise at least one of:

region;

subregion;

work state;

metropolitan and micropolitan statistical area codes;

combined metropolitan statistical area codes;

North American Industry Classification System codes;

industry sector;

industry subsector;

industry supersector;

industry combo;

industry crosssector;

employee headcount band;

employer revenue band;

job title;

occupation;

job level; or

tenure.

15 . The system of claim 10 , wherein the wide-and-deep model is trained through transfer learning.

16 . The system of claim 10 , wherein the linear wide part of the model assists the deep part of the model with residual learning.

17 . The system of claim 10 , wherein cross terms provide sharing information between pairs of dimensions, and wherein dimensions are added to correct for the outliers in the wage data.

18 . The system of claim 10 , wherein dimension embeddings map benchmark dimensions to lower-dimensional vectors, wherein categories predefined as similar to each other have values within a predefined proximity at one or more coordinates.

19 . A computer program product for predictive benchmarking, the computer program product comprising:

a non-volatile computer readable storage medium having program instructions embodied therewith, the program instructions executable by a number of processors to implement a neural network to perform the steps of:

collecting wage data from a number of sources, wherein the wage data comprises a number of dimensions;

preprocessing the wage data;

training a wide linear part of a wide-and-deep model emulate benchmarks and to memorize exceptions and co-occurrence of dimensions in the wage data;

training a deep part of the wide-and-deep model to generalize rules for wage predictions across employment sectors based on relationships between dimensions, wherein the deep part is trained concurrently with the wide linear part;

receiving a user request for a number of wage benchmark forecasts;

forecasting a number of wage benchmarks, wherein linear coefficients produced by the wide linear part are summed with nonlinear coefficients produced by the deep part according to parameters in the user request; and

displaying the wage benchmark forecasts.

20 . The computer program product of claim 19 , wherein wage benchmarks comprise at least one of:

average annual base salary;

median annual base salary;

percentiles of annual base salary;

average hourly rate;

median hourly rate; or

percentiles of hourly rate.

21 . The computer program product of claim 20 , wherein the wide-and-deep model uses linear regression to calculate average base salary.

22 . The computer program product of claim 20 , wherein the wide-and-deep model uses quartile regression to calculate percentile of base salary.

23 . The computer program product of claim 19 , wherein the dimensions comprise at least one of:

region;

subregion;

work state;

metropolitan and micropolitan statistical area codes;

combined metropolitan statistical area codes;

North American Industry Classification System codes;

industry sector;

industry subsector;

industry supersector;

industry combo;

industry crosssector;

employee headcount band;

employer revenue band;

job title;

occupation;

job level; or

tenure.

24 . The computer program product of claim 19 , wherein the wide-and-deep model is trained through transfer learning.

25 . The computer program product of claim 19 , wherein the linear wide part of the model assists the deep part of the model with residual learning.

26 . The computer program product of claim 19 , wherein cross terms provide sharing information between pairs of dimensions, and wherein dimensions are added to correct for the outliers in the wage data.

27 . The computer program product of claim 19 , wherein dimension embeddings map benchmark dimensions to lower-dimensional vectors, wherein categories predefined as similar to each other have values within a predefined proximity at one or more coordinates.