Beaten
by Facebook !
Was Facebook harvesting – and selling – your personal
data ( eg: your friends/contact lists ) for many years now ?
Answer is : Unequivocal , YES !
For details, read :
Extract :
The origins of
this controversy can be traced back to Facebook’s launch of an application
programming interface (or API) for app developers in 2008. With this platform,
Facebook aimed to “lock in” users by organizing its users social lives not just
on Facebook, but across a range of popular apps and services.
To do this,
Facebook granted developers liberal access to its user data so they could build
social features in their own apps. Rather than creating an account and then
requiring users to painstakingly re-friend everyone on Spotify, or Pinterest,
or Instagram, these services sped adoption by letting you quickly import your
existing social graph from Facebook.
If your
friends weren’t yet on these sites, Facebook gave developers your friend list
so you could invite them.
Along with your friend list, the API would return other
information about your friends — specifically, the other pages they liked. This was done
to help you find things you liked and friends with similar interests.
In one API
call, you could get an astonishing amount of data if you wanted it — your friend list and all the pages
they liked, which amounted to potentially tens of thousands of data points per
app authorization.
Popular apps were able to recreate Facebook’s “social graph” — showing
who was friends with whom — as
well as the so-called “interest
graph”,
showing who liked what, and how these likes
related to one another. This might let you see how one’s choice of liquor
correlated with their choice of political candidate, for instance.
Here is what I requested Prof Deshmukh ( IIT – Powai )
in 2013
{ In all fairness , he declined to work on my suggestion
, but I have no doubt someday soon , some Job Portal would develop / launch PA
RANK }
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 ) :
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 !
22 March 2018
www.hemenparekh.in
/ blogs
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