Dear Friends,
You might like my today’s following blog
With Regards,
hemen
parekh
(
M ) +91 - 98,67,55,08,08
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
No comments:
Post a Comment