The Conference for Machine Learning Innovation

AI-powered learning recommendation system: a map and match approach

Session
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Register until October 20:
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✓ Team discount
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Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos
Tuesday, November 29 2022
17:00 - 17:45

Among the challenges faced by Human Resources (HR) professionals is the ability to match learning programs to career paths or to individual employees. At the core of this obstacle is the difference between the terms used to describe a training course and those used in the HR systems to describe jobs and career paths. Simply put, no standard terminology is used, either by the employees in the corporate system or by the learning industry professionals. To alleviate this problem, we have created a mapping & matching framework which allows to automatically connect learning with the other areas of corporate talent management. Our solution relies on a standardized database of 3,000 occupations and 13,000 skills. Mapping algorithms are built (i) to identify the occupations which best correspond to a user’s job title (present or desired) and (ii) to identify the skills referred to in a course description. Matching algorithms take the mapping results to estimate the similarity between every single course and user’s profile and provide the top courses that best match to a given user’s profile. The level of accuracy achievable at each step and their variation with some of the properties of the input parameters will be discussed.

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Behind the Tracks