It’s my daily routine. I get ready for work at 8 am, call an auto to take me to the office and talk to auto-wala for 45 minutes while he drives his dusty old in a busy Mumbai morning. Some are old men in white with big tummies who prate about government policies while some are young and bold trying to match the speed of nearby cars. One talks about how his son is doing, the other how this city is changing, another just frustrated by urban civilisation wanting to go to the village. These little chit chats are part of me now.
The reason why I am writing this is that, even after so many advancements in the field of technology, they are still not visible at grass root level. People like them don’t need personal assistants, they need better jobs.
So, we feel we have much more to give, to use AI in such places where it directly impacts the commons and not just the elites. And it’s not as simple as it looks like. So how do we go about this? The Indian demographics is so huge and versatile that meeting each and everyone’s’ expectation is a tricky task. So, we took connecting these job seekers with jobs as our mission.
Respecting both job seeker expectations and job requirement while reaching to a consensus where both parties can be benefited by it, we developed a recommendation system which takes more than 30 parameters like Job Role, Location, Age, Experience, Gender, Proficiency Level, Salary etc. and then compares them to find the best possible match.
The most important part about implementing recommendation system is that the user should have an intuitive understanding of why and how a particular recommendation is provided. This means users should have full knowledge of why a certain recommendation is shown to them.
We use the Neo4j graph database to fulfill our recommendation needs. Every entity present in our system and relevant for recommendation is used as a Node in graph and relations between them are represented using edges. We use Graphaware Reco framework to modularize and calculate our recommendations. The search space is reduced using various complex engines which churns out relevant candidates. We then calculate the recommendation score using our proprietary algorithm to compare jobs and candidates. We calculate over 100 metrics on the basis of which we calculate the final score.
Context is very important in calculating the recommendations. We prioritise and tune our hyperparameters w.r.t. these contexts and score accordingly. For e.g. for blue-collar workers, location is very important. So while calculating recommendation for blue-collar job seeker, location is super important. Whereas for white collar people, keywords are generally more sensitive data to look for.