How Machine Learning Improves Job Aggregation For Employment Sites

Job boards use machine learning (ML) in sourcing job profiles from countless job aggregators, client websites and niche recruiting platforms. 

Machine learning in recruitment has truly improved job aggregations, thanks to easy data extraction, cleansing, reduction in redundancies, real-time job status updates and more.

Job aggregators, popularly known as job boards, act as a bridge between employers and seekers. A job board lists countless job openings where a job seeker would search and apply for the profiles they want to contest. However, posting jobs for an aggregator is a perplexing task because of several reasons. There are an exorbitant number of openings to account for, they expire quickly and without a trove of postings available at any given time, a job board would cease to exist soon. Scraping is a technique used to find relevant profiles and add them to the boards; however, compiling a large number of profiles is tricky for human scrapers. That’s where machine learning in recruitment comes into play.

Web scraping deploys artificial intelligence and machine learning techniques to find suitable profiles from numerous job boards such as LinkedIn, Monster, Naukri, Glassdoor, Indeed, etc. Using AI and ML does offer employment sites an edge over their competitors while helping them stay relevant with job postings that actively help both employees and job seekers. Utilizing AI in recruitment for job aggregation is a complex subject, so let’s explore the fundamentals of data extraction and the use of AI in detail below.

How AI In Recruitment Improves Job Aggregation For Employment Sites

Instead of manually listing information and then feeding it to a job posting, data extraction techniques such as APIScrapy scrape data from numerous websites, including Monster, Glassdoor, Indeed, etc. The more postings a website has, the more people will visit it, and that’s why it is essential to extract data. Using AI makes the task straightforward as you have to mention guidelines, provide initial training, and then the AI takes care of everything else almost automatically. It finds patterns between the datasets and builds a job posting consisting of company name, job type, job description, reviews, location, job title, salary provided, etc. All this information can be downloaded for data cleansing before posting it. Here are more benefits of AI in recruitment:

1. Enhancing the Quality of the Portal

For an employment site, job openings are a valuable asset and thus, it is crucial to keep them as relevant as possible. Employment sites usually update their portal every day to remove expired job ads and publish new ones from prestigious companies, such as Apple. If an expired job profile is circulating on a job board, its reputation will go for a toss, given as it will damage it extensively.

2. Reducing Redundancies and Promotes Correctness

The use of AI is instrumental in reducing redundancies when posting ads or information on a particular job profile. Manually filling out information may cause redundancies because we are talking about hundreds of thousands of job profiles that companies post every day. Tracking each one is a momentous task that an AI can handle with enough computation power. 

A data extraction AI from MIT uses a ‘confidence score’ to rate the information extracted, and only if it meets specific criteria is the data regarded as successful. It uses a reward and penalties system called ‘reinforcement learning’ to promote the AI’s ability to pick up the relevant and correct information on everything sent out for scouring.

3. Keeping the Job Listings Updated

Scraping job profiles across countless sources enables updating the vacancies in real-time. Depending upon how often the resource websites are crawled, the probability of expiring a job listing varies.

4. Helping Learn Hiring Patterns

Glassdoor is a popular job aggregator where you will find information on ongoing vacancies, salaries offered, reviews, and insights into the company, and that’s all before applying for a job profile. For a job board, it can use the data to learn about hiring patterns, and salaries offered for different profiles and update it on the website. It acts as a magnet for job seekers who would check out a job board with all information in one place rather than look for different sources. 

Data extraction and scraping techniques have evolved a lot over the years. With AI, ML, and Big Data, among other technologies in the works, job boards have found goldmines when scouring the internet for job postings and a trove of data. AI in recruitment is an impeccable tool for data extraction, cleansing, and updates almost without the intervention of a human being.