Course ingestion and recommendation
A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein are directed to a Course Ingestion Engine (hereinafter “C.I. Engine”) that extracts a least a portion of a word present in a course description of an online course. The C.I. Engine determines, based on the at least one extracted portion of the word, at least one skill defined in a social networking service that can be acquired from content of the online course. The C.I. Engine recommends the online course to a target member account of the social networking service.
1. A computer-implemented method comprising:
extracting a least a portion of a word present in a course description of an online course by extracting at least a first word portion from a pre-defined first section of the course description; and
extracting at least a second word portion from a pre-defined second section of the course description;
based on the at least one extracted portion of the word, determining at least one skill defined in a social networking service that can be acquired from content of the online course by, at an application logic layer:
comparing the extracted first word portion and the extracted second word portion to a listing of skills;
identifying a first match between a first skill in the listing of skills and the extracted first word portion;
identifying a second match between a second skill in the listing of skills and the extracted second word portion;
calculating a respective skill score for each of the first match and the second match by:
calculating a first occurrence count based on each instance of the extracted first word portion present in the pre-defined first section;
identifying a first weight coefficient representing an importance of the pre-defined first section;
calculating a first skill score based at least on the first occurrence count and the first weight occurrence;
calculating a second occurrence count based on each instance of the extracted second word portion present in the pre-defined second section;
identifying a second weight coefficient representing an importance of the pre-defined second section; and
calculating a second skill score based at least on the second occurrence count and the second weight occurrence; and
ranking the respective skill scores; and
generating a recommendation in a graphical user interface in a front-end layer based on the respective skill scores.
2. The computer-implemented method as in claim 1 , wherein generating a recommendation comprises:
generating a description resource within the social networking service;
including a selectable link to the online course in the description resource;
receiving, during a session within the social networking service, a request from the target member account to access the description resource; and
providing display data based on the description resource to the target member account during the session within the social networking service.
3. The computer-implemented method as in claim 2 , wherein the description resource comprises:
a job description resource for a job that requires the at least one skill acquired from content of the online course.
4. The computer-implemented method as in claim 2 , wherein the description resource comprises:
a topic description resource comprising a portal within the social networking service to at least one article that corresponds to a topic, at least one job that corresponds to the topic and at least one expert on the topic, wherein the topic further corresponds to the at least one skill acquired from content of the online course.
5. The computer-implemented method of claim 1 , wherein the graphical user interface is displayed on a mobile device.
6. A non-transitory computer-readable medium storing executable instructions thereon, which, when executed by a processor, cause the processor to perform operations including:
extracting a least a portion of a word present in a course description of an online course by extracting at least a first word portion from a pre-defined first section of the course description; and
extracting at least a second word portion from a pre-defined second section of the course description;
based on the at least one extracted portion of the word, determining at least one skill defined in a social networking service that can be acquired from content of the online course by, at an application logic layer:
comparing the extracted first word portion and the extracted second word portion to a listing of skills;
identifying a first match between a first skill in the listing of skills and the extracted first word portion;
identifying a second match between a second skill in the listing of skills and the extracted second word portion;
calculating a respective skill score for each of the first match and the second match by:
calculating a first occurrence count based on each instance of the extracted first word portion present in the pre-defined first section;
identifying a first weight coefficient representing an importance of the pre-defined first section;
calculating a first skill score based at least on the first occurrence count and the first weight occurrence;
calculating a second occurrence count based on each instance of the extracted second word portion present in the pre-defined second section;
identifying a second weight coefficient representing an importance of the pre-defined second section; and
calculating a second skill score based at least on the second occurrence count and the second weight occurrence; and
ranking the respective skill scores; and
generating a recommendation in a graphical user interface in a front-end layer based on the respective skill scores.
7. The non-transitory computer-readable medium as in claim 6 , wherein generating a recommendation comprises:
generating a description resource within the social networking service;
including a selectable link to the online course in the description resource;
receiving, during a session within the social networking service, a request from the target member account to access the description resource; and
providing display data based on the description resource to the target member account during the session within the social networking service.
8. The non-transitory computer-readable medium as in claim 7 , wherein the description resource comprises:
a job description resource for a job that requires the at least one skill acquired from content of the online course.
9. The non-transitory computer-readable medium as in claim 7 , wherein the description resource comprises:
a topic description resource comprising a portal within the social networking service to at least one article that corresponds to a topic, at least one job that corresponds to the topic and at least one expert on the topic, wherein the topic further corresponds to the at least one skill acquired from content of the online course.
10. A computer system, comprising:
a processor;
a memory device holding at least one instruction set executable on the processor to cause the computer system to perform operations comprising:
extracting a least a portion of a word present in a course description of an online course by extracting at least a first word portion from a pre-defined first section of the course description; and
extracting at least a second word portion from a pre-defined second section of the course description;
based on the at least one extracted portion of the word, determining at least one skill defined in a social networking service that can be acquired from content of the online course by, at an application logic layer:
comparing the extracted first word portion and the extracted second word portion to a listing of skills;
identifying a first match between a first skill in the listing of skills and the extracted first word portion;
identifying a second match between a second skill in the listing of skills and the extracted second word portion;
calculating a respective skill score for each of the first match and the second match by:
calculating a first occurrence count based on each instance of the extracted first word portion present in the pre-defined first section;
identifying a first weight coefficient representing an importance of the pre-defined first section;
calculating a first skill score based at least on the first occurrence count and the first weight occurrence;
calculating a second occurrence count based on each instance of the extracted second word portion present in the pre-defined second section;
identifying a second weight coefficient representing an importance of the pre-defined second section; and
calculating a second skill score based at least on the second occurrence count and the second weight occurrence; and
ranking the respective skill scores; and
generating a recommendation in a graphical user interface in a front-end layer based on the respective skill scores.
11. The computer system as in claim 10 , wherein generating a recommendation comprises:
generating a description resource within the social networking service;
including a selectable link to the online course in the description resource;
receiving, during a session within the social networking service, a request from the target member account to access the description resource; and
providing display data based on the description resource to the target member account during the session within the social networking service.
12. The computer system as in claim 11 , wherein the description resource comprises:
a job description resource for a job that requires the at least one skill acquired from content of the online course.