Building a Job Recommender for Non-Technical Business Roles via NLP and Machine Learning

Building a Job Recommender for Non-Technical Business Roles via NLP and Machine Learning

In this post we will be using datasets hosted by Kaggle and considering the content-based approach, we will be building job recommendation. A tutorial for a NLP recommendation engine using unsupervised learning .

I was inspired to build a job recommender because of recent layoffs at my old company. Many of my former colleagues who were let go were less than 3 years into their careers, and they wondered, based on their experiences, what roles they fit in and where they should start their job search. I remembered a similar struggle coming out of college, where I didn’t really know where to focus my search either. I believe that this issue is quite common for people with non-technical backgrounds.

I thus decided to build a job recommender for people in our positions — those with non-technical backgrounds like a Psychology major — that would use information based on our past experiences to help steer us in the correct path.

The app combines NLP techniques such as topic modeling with classification-style machine learning in order to determine the best fit for you. You copy and paste your resume / LinkedIn into the text box, and the app parses the text and presents you with ML-driven analysis of which jobs you fit and why.

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