FW: FW: A Brave New World

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

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

From: Hemen Parekh
Sent: Saturday, February 16, 2013 5:36 AM
To: 'deshmukh.rahul@iitb.ac.in'; Manjunath, Geetha (HP Labs India); shuklendu.baji@sentientsystems.net
Subject: A Brave New World

Dear  Team

Take a look at the following news report

That made me search in Google – and in all cases, found topmost entry at Wikipedia ( crowd sourcing , at its best ? ), the following :

Ø The Brave New World

Ø Karmarkar’s Algorithm

Ø Travelling Salesman Problem

Ø Swarm Intelligence

Ø Neural Network……….etc

In the article below , the company ( Relationship Science ),

Ø Does not depend upon “ user-generated content “,  …….but

Ø Scrapes the web for building its database

In either case , as Scott McNally ( Sun Micro ) famously said,

“ In internet age, privacy is dead. Period “

In our next app ( Resume Blaster ), Shuklendu has incorporated a Cookie which will pick-up the entire “ Contact Data “ from the smart phone of each user

Hopefully , someday in a later , non-commercial version of “ My Jobs “ , Prof Rahul Deshmukh will incorporate a similar cookie

And I suppose , in still later versions of both , we will succeed in picking-up the entire smart phone logs of voice-mails and text messages

I have absolutely no doubt that many apps are already doing this – so , if we ever get around to doing it , we will be merely “ re-inventing the wheel “ !

That will leave someone to write an algorithm that determines which “ Neural Connections “ are Strong / Average / Weak , based on the frequency and duration of the messages exchanged between them

Then , we would have beaten Goldman and his company, Relationship Science !

I may not be around to see this but I hope the idea survives

Regards

Hemen

Source : Economic Times / Feb 13 , 2013 ( NYT News Service )

Andrew  Ross  Sorkin

A New Database of Names could Change Wall Street

It sounds like a Rolodex for the 1%: 2 million dealmakers, power brokers and business executives – not only their names , but in many cases the names of their spouses and children and associates , their political donations , their charity work and more – all at a banker’s fingertips.

Such is the promise of a new company called Relationship Science. Never heard of it ? Until recently , neither had I. But a few months ago, whispers began that this young company was assembling a vast trove of information about big names in Corporate America. What really piqued my interest was that bankrolling this startup were some Wall Street heavyweights, including Henry R Kravis, Ronald O Perelman , Kenneth G Langone, Joseph R Perella, Stanley F Druckenmiller and Andrew Tisch.

It turns out that over the last two years, with a staff of more than 800 people, mostly in India, Relationship Science has been quietly building what it hopes will be the ultimate Who’s Who. If it succeeds, it could radically change the way Wall Street does business.

That is a big if , of course. There are plenty of other databases out there. And , there is always Google. Normally I wouldn’t write about a technology company, but I kept hearing chatter about it from people on Wall Street. Then I got a glimpse of this new system. Forget Six Degrees of Kevin Bacon. This is Six Degrees of Henry Kravis.

Here’s how it works:

Let’s say a banker wants to get in touch with Kravis, the private equity dealmaker, but doesn’t know him personally.

The banker can type Kravis’s name into a Relationship Science’s search bar , and the system will scan personal contacts for people the banker knows who also know Kravis, or perhaps secondary or tertiary connections.

The system shows how the searcher is connected – perhaps a friend , or a friend of a friend, is on a charitable board – and also grades the quality of those connection by identifying them as “ strong “, “ average “ or “ weak “.

The major innovation is that, unlike Facebook or LinkedIn, it doesn’t matter if people have signed up for the service. Many business leaders aren’t on Facebook or LinkedIn , but Relationship Science doesn’t rely on user-generated content. It just scrapes the Web.

Relationship Science is the brainchild of Neal Goldman, a co-founder of CapitalIQ, a financial database service that is used by many Wall Street firms. Goldman sold CapitalIQ, which has 4200 clients worldwide, to McGraw Hill in 2004 for more than $ 200 million. That may explain why he was able to easily round up $ 60 million in funds for Relationship Science from many boldface names in finance. He raised the first $ 3 million in three days.

“ I knew there had to be a better way,” Goldman said about the way people search out the others. Most people use Google to learn about people and ask friends and colleagues if they or someone they know can provide an introduction. Relationship Science essentially does this automatically. It will even show you every connection you have to a specific company or organization.

“ We live in a service economy,” Goldman said. “ Building relationship is the most important part for selling and growing.”

Kenneth Langone, a financier and co-founder of Home Depot, said that when he saw a demonstration of the system he nearly fell off his chair. He used an unprintable four-letter word.