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