How to Rank Resumes ?

In an email to Prof Rahul Deshmukh ( IIT – Mumbai ) , I had suggested following approach :

Feb 18 , 2013

Dear Team

Google calls it PageRank – to decide the importance of a web page – then use this number to decide the rank ( position ) of that page within the search results

Let us follow Google ( very safe ! )

And call our “ Ranking Mechanism “ of any given resume , a “ Pa-Rank “

Then arrange all arriving resumes ( in the mailbox of a Recruiter or on our web site ), in the descending order of their Pa-Rank

Highest ranked at the top

So , the Recruiter need to open / read only the top 5 - 7  !  No  more  !

In much the same manner that 95% of the searchers never go beyond the first 3 pages of any search-result despite millions of pages

Just imagine how much time it will save the recruiters to zero-in on the best candidates from amongst hundreds applying for any given job

This has the potential to revolutionize the On-line recruitment industry !

How can we determine a candidate’s Pa-Rank ?

Just like PageRank , which takes into account,

#   No of Incoming links to a given page

#   Importance of each incoming link ( based on its own incoming links )

No doubt , Google’s actual algorithm must be very complex – and continues to evolve

Thru our mobile app , “ Resume Blaster “ ( and someday , thru app “ My Jobs “ as well ), we pick-up the “ Contact List “ ( of mobile nos ) of say, Ajay ( a user registered on our site )

These are his “ Primary Incoming Links “ – say , 40

Out of these 40 , say , 10 have also registered on our site AND , are also using our mobile app “ Resume Blaster “

In turn , these 10 have , between them , 600 contacts ( “ Secondary Incoming Links “ )

So , for Ajay , total Incoming links =  40+600 =  640

Now , if Ajay thinks he has figured out how our software computes Pa-Rank then he will somehow find out the mobile number of Mukesh Ambani , and add into his “ Contacts “ – assuming that at one stroke ,he can add ONE MILLION incoming links to himself !

But , we know that such high-flying executives are unlikely to have registered on our site – much less , to be using our app , “ Resume Blaster “ !

Hence we treat this as a “ Dead Incoming Link “

On top of that , we can further refine our Pa-Rank based on,

#   The frequency with which Ajay keeps calling each of those 10 persons ( who have registered on our site AND are also using our app “ Resume Blaster “ )

#   The duration of each call ( or even text message ? )

We can multiply / sum-up / find average ,  etc of such “ exchanges of messages – both ways “ and arrive at some “ FACTOR “

Then multiply the “  Total No of Live Incoming Links “ with this “ FACTOR “ , to compute PaRank

Looks crude , no doubt !

But Google has proved that it works !

What PageRank is for all the web pages in the World , someday , PaRank will be to all the resumes ( which too , like any web page , are documents ) , in India

We must do it !

On internet , “ Ideas are dime-a-dozen “

But elegant executions are rare !

8 years earlier ( on 21 Dec 2005 ) , I had sent the following proposal to the Shri Ashish Kashyap ( the then Country Manager - Google India ) :

GoogleJobs.com

{ http://hcpreports.blogspot.com/2016/02/google-jobs.html }

It was only this week that Google launched “ Google Job Search “ ( for USA jobs )

I am sure someone in Google will read this blog – then implement it to change the rules of Online Recruitment Industry !

23  June  2017

www.hemenparekh.in / blogs