Calculating supply and demand for a field of study

Patent metadata

Field	Value
Publication number	US-2017032037-A1
Application number	US-201514815557-A
Country	US
Kind code	A1
Filing date	Jul 31, 2015
Priority date	Jul 31, 2015
Publication date	Feb 2, 2017
Grant date	—

Abstract

Techniques for presenting a recommendation to a member of a social network in a specific field of study are described. A predictor can access, from a database in the social network, educational data of a plurality of students and post-graduate data of a plurality of graduates in the social network. Additionally, the predictor can determine a subset of students associated with a specific field of study, and can calculate a demand for the specific field of study based on the accessed data. A recommendation generator can calculate a competition value for the specific field of study based on the determined subset of students and the calculated demand for the specific field of study. Subsequently, the recommendation generator can cause a presentation, on a display of a device, of a recommendation associated with the specific field of study, the recommendation being based on the calculated competition value.

First claim

What is claimed is:

1 . A method comprising:
accessing, from a database in a social network, educational data of a plurality of students in the social network, the educational data including an academic major associated with a field of study; determining, from the plurality of students, a subset of students associated with a specific field of study based on the accessed educational data; accessing post-graduate data of a plurality of graduates in the social network; calculating a demand for the specific field of study based on the accessed post-graduate data; calculating, using a processor, a competition value for the specific field of study based on the determined subset of students associated with the specific field of study and the calculated demand for the specific field of study; and causing a presentation, on a display of a device, of a recommendation for a student from the determined subset of students associated with the specific field of study, the recommendation being based on the calculated competition value.

2 . The method of claim 1 , wherein the calculating of the demand for the specific field of study includes:
calculating a number of job openings for the specific field of study based on the accessed post-graduate data, the accessed post-graduate data having employment data of the graduates; and wherein the demand for the specific field of study is based on the calculated number of job openings available for the field of study.

3 . The method of claim 1 , wherein the calculating of the demand for the specific field of study includes:
calculating a number of graduate students enrolled in a graduate school for the specific field of study based on the accessed post-graduate data, the accessed post-graduate data having graduate school enrollment data for the graduates; and wherein the demand for the specific field of study is based on the calculated number of graduate students.

4 . The method of claim 1 , wherein the post-graduate data is accessed for the college graduates that have graduated from college less than two years ago.

5 . The method of claim 1 , wherein the competition value for the specific field of study is calculated by dividing the determined subset of students associated with the specific field of study by the calculated demand for the specific field of study.

6 . The method of claim 5 , wherein the competition value is high when the competition value is above a predetermined threshold value, and wherein the recommendation is to switch to a different field of study when the competition value is high.

7 . The method of claim 5 , wherein the competition value is low when the competition value is below a predetermined threshold value, and wherein the recommendation is to stay in the specific field of study when the competition value is low.

8 . The method of claim 1 , further comprising:
determining a number of different job types for the specific field of study; and calculating a dispersion value for the specific field of study based on the determined number of different job types, wherein the recommendation is further based on the calculated dispersion value.

9 . The method of claim 8 , wherein the recommendation is to stay in the field of study when the dispersion value is below a predetermined threshold.

10 . The method of claim 8 , wherein the recommendation is to switch to a different field of study when the dispersion value is above the predetermined threshold.

11 . The method of claim 1 , wherein the recommendation is related to an internship.

12 . The method of claim 1 , wherein the recommendation is to take a specific course.

13 . The method of claim 1 , wherein the recommendation is to become certified in a specific skill.

14 . The method of claim 1 , wherein the students are college students, and wherein the graduates are college graduates.

15 . A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
accessing, from a database in a social network, educational data of a plurality of students in the social network, the educational data including an academic major associated with a field of study; determining, from the plurality of students, a subset of students associated with a specific field of study based on the accessed educational data; accessing post-graduate data of a plurality of graduates in the social network; calculating a demand for the specific field of study based on the accessed post-graduate data; calculating, using a processor, a competition value for the specific field of study based on the determined subset of students associated with the specific field of study and the calculated demand for the specific field of study; and causing a presentation, on a display of a device, of a recommendation for a student from the determined subset of students associated with the specific field of study, the recommendation being based on the calculated competition value.

16 . The storage medium of claim 15 further comprising instructions that cause the machine to perform operations comprising:
calculating a number of job openings for the specific field of study based on the accessed post-graduate data, the accessed post-graduate data having employment data of the graduates;
calculating a number of graduate students enrolled in a graduate school for the specific field of study based on the accessed post-graduate data, the accessed post-graduate data having graduate school enrollment data for the graduates; and
wherein the demand for the specific field of study is based on the calculated number of job openings available for the field of study and the calculated number of graduate students.

17 . The storage medium of claim 15 wherein the competition value for the specific field of study is calculated by dividing the determined subset of students associated with the specific field of study by the calculated demand for the specific field of study.

18 . The storage medium of claim 17 wherein the competition value is high when the competition value is above a predetermined threshold value, and wherein the recommendation is to switch to a different field of study when the competition value is high.

19 . The storage medium of claim 15 further comprising instructions that cause the machine to perform operations comprising:
determining a number of different job types for the specific field of study; and
calculating a dispersion value for the specific field of study based on the determined number of different job types, wherein the recommendation is further based on the calculated dispersion value.

20 . A social network system comprising:
one or more member profile databases having educational data and post-graduate data, the educational data including an academic major associated with a field of study; one or more processors in a predictor to:
access the educational data of a plurality of students in the social network system;
determine, from the plurality of students, a subset of students associated with a specific field of study based on the accessed educational data; and
access the post-graduate data of a plurality of graduates in the social network system;
calculate a demand for the specific field of study based on the accessed post-graduate data;
one or more processors in a recommendation generator to:
calculate a competition value for the specific field of study based on the d

Assignees

• Linkedin Corp

Inventors

• Goel Mudit
• Feng Yi
• Wang Weizhen
• Li Dong

Classifications

• G06Q50/2057

  Career enhancement or continuing education service · CPC title

• G06F16/951 Primary

  Indexing; Web crawling techniques · CPC title

• G06F16/9536 Primary

  Search customisation based on social or collaborative filtering · CPC title

• G06F17/30864 Primary

  Physics · mapped topic

• G06F17/3053

  Physics · mapped topic