We take suggestions / recommendations from our near and dear as they know what we like and what fits us best. This is what Jobgar ‘Recommendation System’ trying to model to deliver the same relevant recommendations.
So how Jobgar’s Recommendation Engine actually works ?
Firstly, 'Smart Algorithm' understand user's preferences and interests and keeps learning in real-time about their changing interests and habits.
Then 'Profiling Technology' comes in, it profiles each user by analyzing user's resume including attributes, demographics, social networks, skills, experience, location, CTC, etc.
Finally, 'Recommendation Engine' does the intelligent predictions using user's skills, experience, current location, current CTC and interests. Engine determines and recommend a user to switch a job whenever a matching opportunity is posted.
While doing so, due diligence is given to individual privacy.
What does Jobgar's Recommendation Engine do?
Recommendation Engine combines user's preferences, resume details and activities, it offers automated personalized advice, tailored to each user, to include:
Job Switch Recommendations
Special Someone Recommendations
Professional Connection Recommendations
Benefits of Jobgar’s Recommendation Engine.
Recommendation Engine is based on actual user behavior to provide the best possible experiences.
It Delivers relevant job postings only.
It reduces the need to perform manual search for better job opportunities.
It also recommends skill enhancement to improve job prospects.
Personalized interactions by the website at every step of the user journey.
A recommendation engine boosts job posting by bringing targeted traffic to it.
Personalization solution for business.
Engages Jobseeker to job postings.
Jobgar is not just a job search or job matching website, but a candid attempt to understand 'Job-seeker' & 'Employer' needs, and constantly interact with them to deliver content / recommendations for better prospects.