CAPTURING JOBSEEKER'S KNOWLEDGE

Rahul

11-04-06

Capturing Jobseekers' Knowledge

(to make GurooMine a self-learning software)

We discussed this today.

In IndiaRecruiter, a jobseeker has to identify 3 industries & 3 functions, where he claims to have strong background.

But,

Which of these 3 (industries & functions) are,

• Most "relevant"? (Where he feels superbly confident to succeed)
• Quite "relevant"? (Where he still feels quite comfortable)
• Somewhat "relevant"? (Where he can "get into the groove" with some brushing-up).

Our existing "Submit Resume" form does not bring-out these subtle differences/nuances between those 3 industries/3 functions.

But, we have a STRONG need to capture these.

To do this, let us modify "Submit Resume" form as follows:

What is your background in terms of:
Industry	Functions	Keywords
[Text box with dropdown]	[Text box with dropdown]	[Text box with dropdown]

 

It is quite unlikely that you feel equally comfortable with your choices of Industries & Functions. Which do you consider,

• Most relevant

[Where you feel superbly confident to succeed] - - - [1]

• Quite relevant

[Where you still feel quite comfortable] - - - [2]

• Somewhat relevant

[Where you can get back into the groove with some brushing-up] - - - [3]

In the box below, please Rank your choices (to help us recommend to you the ideal jobs)

My Ranking is as follows

	Industry	Type Rank	Function	Type Rank
		[3]		[2]
		[1]		[1]
		[2]		[3]

[Large Text Box for Resume]

Cut & Paste your text resume in the box below

Once we capture the ranking, we will store these in our database, against the name of the concerned candidate.

That will enable us to create "sub-populations" of candidates

• Industry-wise
• Function-wise

Next Step

For all candidates belonging to Industry ABC, add-up all the keywords contained in their "Knowledge Profile" boxes.

Then calculate

• "Frequency of Occurrence" of each of those keyword (probability of occurrence).

Since, each candidate has, identified himself as. belonging to

 Industry = ABC

 Function = XYZ

and, he has himself used/selected certain "keywords" in his resume ($\therefore$ in Knowledge Profile box),

we can safely assume that these keywords belong to those Industry/Functions!

So, now, instead of One or two "Experts" deciding

"Which keywords signify/denote which Industry? ... which Function? ..."

.... we get thousands of real experts (i.e. the candidates themselves) to certify this relationship (between "keywords" on one hand, and "Ind/Func" on other $\left.\right)$

This is exactly the future path of YAHOO's search engine, viz:

evolve a "Social Consensus" thru a large no. of USERS voting/ranking/rating on items' importance/relevance to the "Search Query".

(Like AUDIENCE POLL in KBC!)

More & more search-engines are adopting this technique to arrange/display search-

results, in the descending order of the "Rank/Score" awarded by previous visitors.

This method (of creating smaller sub-populations) will also dramatically reduce software's burden of computing "Frequency of Usage". This is because total Candidate population (of say, a million resume), will now (possibly) get broken up into 30,000 resume sub-populations (30 of them)!

Within each sub-population's "Keywords", quite likely, the top 50 (arranged in descending order of frequency-of-usage), will add-up-to 90% of the sum-total of probability (i.e. add up to $0.9$ probability). Subsequently, for plotting percentile graphs, we need to use, only these top 50 (or 60 or 40) Keywords for matching/finding from next arriving resume, to give Raw Score.

Fresh computing of "Frequency of Usage" taking ALL keywords in any given "sub-population" (of Industry or function), may be done once-a-week (over the weekend?).

[Signature and Date: 11/04/06]