Hi Friends,

Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do. There is just no time to look back, no time to wonder,"Will anyone read these pages?"

With regards,
Hemen Parekh
27 June 2013

Now as I approach my 90th birthday ( 27 June 2023 ) , I invite you to visit my Digital Avatar ( www.hemenparekh.ai ) – and continue chatting with me , even when I am no more here physically

Wednesday 16 March 2016

Pa - Rank


Dear Kunal,

I copied from the attachment sent to you earlier , the following email re Pa-Rank

regards,

hemen

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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

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,

Ø No of Incoming links to a given page
Ø 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 Resume Blaster ( and someday , thru 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 using 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 Resume Blaster !

Hence we treat this as a “ Dead Incoming Link

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

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

Regards

hemen






hemen  parekh


Marol , Mumbai , India


( M ) +91 - 98,67,55,08,08


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