Those days of finding jobs on weekly newspapers are long gone. While everything was going digital, this tedious process had to evolve as well, but the new way of landing jobs proved to be far easier yet sophisticated to even be compared with the newspaper job hunt or the far worse routine … flyers! In any event of a job hunt, there are majorly three parties involved; the employer, the applicant, and the recruiter/platform. The manual method was inconvenient for all three. It took ages for the employer to find the right skillset while was a pain for the applicant to skim through hundreds of job ads in at least tens of newspapers. For the recruiter, the business was just unscalable. Thanks to technology, the process of job matching is now so automated that most of the time you get such tailor-made job opening notifications, that it possibly can prompt you for a job switch.
In this procedure of magical matchmaking, we prefer to call cross-validation, there is a collection of machine learning algorithms working in the background. There are 5 leading ways to configure the machine learning process for an efficient job recommendation system.
TF-IDF and Word2vec Based Job Matching
This method is a combination of two separate functions, TF-IDF and Word2vec, performed systematically to later analyze the computed data with similarity functions.
The TF-IDF is used to evaluate the importance of a keyword in any content and assign the value to that keyword based on its frequency of appearance in the analyzed document. It also evaluates how relevant the keyword is throughout the web, which is referred to as corpus.
Word2Vec is a word embedding algorithm that relies on shallow neural network to vectorize the text in a corpus.
After running these two algorithms on the two sets of data, job and applicant details, the results are now ready for data matching. One of the similarity functions will be applied to the collected data to get top-N similar job vectors.
Locality Sensitive Hashing (LSH) is a well-suited framework to develop this model. It is a technique that collects similar input items into the same “buckets” with high probability. This technique can be used for data clustering and nearest neighbor search.
Personalized and Localized Job Recommendations
This is a simpler matchmaking algorithm. With a set of applicants and a set of job features, the task is to match users to their preferred job features. There are two approaches to this type of matching: content-based and location-based. Both approaches have pros and cons to them, and there are also ways to combine them to take advantage of both the techniques.
Word2vec comes in handy with this method as well and plays a similar role by converting the text in both resumes and job postings such as skills, experiences, services, current city, etc. into numerical feature vectors.
LSH or k-NN (k-Nearest Neighbour) are the recommended algorithm framework to predict similar jobs based on personalization and/or localization.
Topic Modelling Via Matrix Factorization
The Natural Language Processing (NLP) based topic modeling method is designed to discover the latent semantic structure or topics within a corpus of documents, which in the case of job matching, includes both job descriptions and resumes. It is derived from the co-occurrences of words across the content of the documents. With matrix factorization, it builds topic hierarchy via splitting larger-topics into sub-topics.
Among the more prevalent approaches for topic modeling, the application of probabilistic algorithms such as Latent Dirichlet Allocation is more popular.
Resume Parsing
Resume parsing is one of the easiest and most common models used by applicant tracking systems in organizations and by recruiters. With this process of resume parsing, all relevant data is extracted from free-form resumes. Tesseract is one of the initial-stage tools for this process. It is an optical character recognition engine that reads text from common CV PDFs or even an image. Once the text is extracted it gets parked in relevant headers of information which can later be matched with relevant job openings based on skills, location, education, or experience.
Current Connections + Approach 1
Using this one of the lesser technical methods can be equally efficient in certain scenarios, especially when the target prospect is to be found from a smaller/limited pool. To apply this method effectively it requires hash mapped data of the whole pool of prospects. If an applicant has 2-3 connections already working in the employing organization, he is considered as a viable option.
Jobs matching the applicants’ skill set can be recommended using a combination of LSH and k-NN techniques.
All the leading methods of job matching discussed above can be built using the highly rated services of AWS’s SageMaker Studio, Textract or Comprehend. Azure and Google Cloud Platform’s machine learning and text analytics solutions are also used by industry leaders.
A job matching model is designed to match jobs to relevant individuals, removing the tiresome need for a manual search. The job recommender should evaluate a person’s suitability for jobs and come up with surgical results with a list of users based on their target skillset and other pre-defined parameters. These are established and proven algorithmic methods based on machine learning that can be engineered to create a productive job recommendation engine on Python.
