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

Thursday 22 March 2018

THIS WILL COME , ONE DAY !


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 friendsspecifically, 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 ityour 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 whomas 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



No comments:

Post a Comment