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

Saturday, 24 June 2017

Of Interest to Recruiters / Job Portals



Yesterday , I attended a seminar on CHAT BOTS , conducted by Beerud Sheth ( www.GupShup.io )



Leaving aside the technical part ( which was beyond me ) , I realized that the time has come for some of my ideas , which I jotted down , way back in Jan 2007 , in the margins of the book :



ARTIFICIAL INTELLIGENCE APPLICATION PROGRAMMING ( M. Tim Jones )


I believe , someday soon , either some start up in the Online Recruitment space or one of the major Job Portal ( Naukri / MonsterIndia / TimesJobs / LinkedIn ), will implement these ideas



I have no doubt , Beerud ( beerud@gupshup.io ) would be able to create appropriate BOTs for each of these ideas ( and , even make these BOTs talk to each other ) , for a few thousand dollars !



I wonder why people are worrying about ROBOTS replacing humans in distant future



To me , the issue is : How many human jobs, will BOTS take away in near future ?



For Recruiters / Headhunters , that answer lies on  Pages  142 / 300 /388  !



Here are those notes scribbled in the margins of the book :




Page  3

·         We are planning that “ weights “ of each keyword gets automatically adjusted / updated dynamically , with arrival of each new resume in our database . We only provide the “ seeds “ – the starting weights . Inputs are “ Keywords “ / Outputs are “ Raw Scores


Page  4

·         This would be of interest to us . Job Advts posted ( on any job site ) are nothing but expressions of a Company’s “ staffing requirements “ . 

If we have 5,000 job advts for a given Company ( over past 3 years ), we should be able to find a time-series / trend and should be able to predict its “ future “ requirements . Simplest is extrapolation of past trends !


Page  47

·         We have talked of a concept like Rubik’s Cube , to place ( ie; bring together ) on each “ Face “ , keywords belonging to a given “ Skill “ or “ Function “

·         Goal = Getting all “ same coloured “ keywords ( squares ) onto the same face of the cube

·         Path = Time taken ( shortest )

·         Each “ face “ of our Virtual Rubik Cube , would have same colour , if someone succeeds in virtually “ rotating “ the layers along the 2 AXIS of freedom , until all squares are of SAME COLOUR – meaning that all squares contain Keywords belonging to SAME SKILL

·         We can “ time “ the game , to see which visitor ( WHO ) , manages to get ALL keywords ( squares ) on the SAME face in the shortest possible time ; then give him recognition / credit by publishing his name on our web site

·         This could be a lot of FUN – and could, possibly draw a lot of young kids to our web site ! 


There will be no login / registration for playing the game . Just walk in and play . Then upload the COMPLETED / SUCCESSFUL image ( with time taken ) and email to all friends to prove your cleverness ! [ 06 JAN 2007 ]



Page  69

·         Particles  =  Keywords

·         Space     =  Text Resume

·         Best found solution  =  Resume with highest score

·         Better one  =   Addition / deletion of  “ which “ & “ how many “ keywords , would result in a Raw Score better than the “ best found “ solution / resume ?



Page   70

·         What keywords ( elements ) are always found “ together “ ?



Page  72

·         Is one resume , a “ solution space “ in which particles ( ie; Keywords ) are swarming around ?


Page  91

·         Set of data   =   Master set of 5,000 keywords

·         Smaller set  =   Function-wise sets of 100 keywords

·         Classification  =  “ Skills “ & “ Functions “ are Classifications



Page  92

·         Read my notes on “ Expert Systems “ / eg: finding keywords which have NEVER occurred before in ANY resume  . 


How will software “ Know “ that it is a NEW keyword ?

 – and then know “ to which new Skill / Function “ does it belong to ?


·         Clusters are , Functions / Skills / Cities / Designation Levels / Edu Qualifications / Experience etc

·         Customer Set : Obviously , WIPRO / Infosys / TCS / Satyam , belong to a well-defined “ customer set “

 . They all have “ Common Attributes “ , ie : similar / identical job advts posted / similar / identical , resumes searched



Page  93

·         Each “ sub set “ can be ,

#  same SKILL / same FUNCTION / same CITY / same EDU etc


·         Corporates are “ purchasing “ resumes from job portals and we will have exhaustive data on their “ purchases “ , ie : Number / Type of resumes transferred to folders / opened / viewed




Page    98

·         We are planning a “ Recommendation  System “ – which would recommend job advts to jobseekers and resumes to Recruiters

·         If Wipro HR manager shortlisted / interviewed such and such candidates , same could also be of interest to Infosys HR manager



Page  113

·         A learning component to identify new rules = I have written some rules


Page  118

·         This is what we are interested in. Starting with ( may be ) 50 rules on recruitment ( getting selected / getting appointed ) , we want the algorithm to discover 500 “ NEW RULES



Page  142

·         Can we develop an “ AGENT “ ( ant ) for each corporate subscriber’s each job advt., which travels to “ distant places “ ( different job sites ) , and safely bring back “ food “ ( resumes ) to its own “ nest “ ( folder ) ?

·         Millions of “ ants “ ( software agents ) , let loose on Web Network, each programmed to find a “ specific “ type of resume – then , when it finds , passing on this data to the next ( adjoining ant / agent ), ie; communicating

Then becoming free again to search for next resume for which it is programmed


A resume may pass from one agent to another ( may be several hundred / thousand ) , till it finally gets received by the ANT / AGENT , who is “ programmed “ to find it in the first place !


So , it is not necessary that an AGENT ANT finds only that resume for which it is programmed , as long as it finds any ONE resume , from ANYWHERE & communicates ( passes onto the next / adjacent Ant )


Like Packet Switched Network ?


All packets getting assembled by the DESTINATION ANT


Parameters stored getting matched with arriving resume’s parameters



Page  143

·         In GURU-JEM ( improved version of HARVESTER ) , we are trying to find web records containing Company Name / Designation,

But from a single source ( viz: Google )


Our software agent travels to Google , finds and brings back the results ( food )


But , could we possibly design / devise , millions of software agents , each programmed to roam the Web ( or , predetermined URLs ) and find,


#  One of the thousands of “ designations “ , OR


#  One of the thousands of “ Company Names , OR


#  One of the lakhs of “ Executive Names “ , OR


#  Any combination of the above ,


Then bring back the results ?



·         Since most job sites permit access to  “ Job Advt Database “ to any visitor without need for a Password, it may be much easier for ANT AGENTS to roam job sites ( like Search Engine Spiders ? ) and bring home suitable Job Advts ( what we do in a limited manner )


Page  165

·         Game  =  Online Recruitment / Searching for Employers / Searching for Candidates

·         Characters  =  Recruiters  /  Jobseekers

·         Agents        =  Job  Advts  /  Resumes

·         Environment   =  Virtual Job Fair /  Virtual Employment  Portal



     { On Line  Jobs  Fair  / 




Page  207

·         I believe Cyril used such an algorithm, to read a plain text resume . The software did manage to accurately “ find “ the “ Address “ ( from anywhere in the resume ), after about 72 hours of continuous “ exploring / processing / learning “


This was nearly 8 years ago . I am sure by now , far more powerful neural-net “ Shells “ can be freely downloaded , which would “ parse “ a plain text resume to find accurately, ALL the “ fields / values “ which we need to create a “ Structured Database “


It is simply a question of experimenting and now , we have no shortage of hardware  [  07  JAN  2007  ]



Page   230

·         Someday ( when we have a million resumes on our web site ), we will have many sub-sets of candidates who started their careers ( first job ) ,

          #  at the same AGE , and with


          #  the same EDU QUALIFICATION



         Question is :  How did their careers “ Evolve “ ?


         What Salary / Designation , did each of them reach / achieve , after 5 years / 10
         years / 15 years ?

  

Was there a “ pattern “ ?


         Did their “ career paths “ run “ parallel “ or did they diverge ?


         What other “ factors “ ( eg: Employer Companies ), influenced such divergence ?


         Did some “ plateau out “ after a time whereas others continued to climb the
        Designation / Salary ladder ?  (  Graph drawn  to illustrate the concept )



Page  300

·         With an ever-expanding set of “ Rules “ , it will be possible for a “ Rule-based “ system to detect fake / fudged resumes , where a candidate is telling a lie re: some facts

·         I have listed several such rules . See my notes in “ Expert System “ . Also my handwritten notes in the margin of book , “ EXPERT SYSTEMS “


·         In each resume , each & every field-value is a “ FACT “ . 

Therefore , facts are , Age / Experience / Designation Level / Edu Quali / City / “ Skill Keywords “ found / Salary / Employer Name  etc


·         One can start with simple rules such as

 ,
#  Experience ( years ) cannot exceed Age


#  Age cannot be less than 16


#  Edu Quali cannot be less than 10th Std / SSC


#  MD ( Managing Director ) cannot be less than GM ( General Manager )


#  Post-Grad cannot be less than  Graduate


#  Salary cannot be less than Rs 1000 pm




Page  335

·         In developing Function Profile grphs, we are simply depending on “ presence “ or “ absence “ of any given Keyword ( Binary Status ) . 

Then assigning a “ weightage “ ( of a given amount of weightage of ZERO ) , depending upon present or absent


But in real world, it may turn out that the Candidate with a lower Raw Score ( because of many “ absent “ keywords ) , turns out to be a better choice ( higher interview-score in Interactive Interview Tool ) , than a candidate with a higher Raw Score ( where most Keywords were present )


In such a scenario , should we try out “ Fuzzy Logic Algorithm “?



Page  372

·         Author’s note :   Consider an email program that monitors the behaviour of the user. When e-mail message arrives, the model observes what the user does with the e-mail message and uses this information to learn how to automatically deal with subsequent e mail message


My comments :   Beysian spam filter ? – based on a starting database of “ unwanted “ keywords in the e mail message



Page  376

·         We must experiment with Kurzweil’s Paraphrasing Software

·         Could Kurzweil’s “ Paraphrasing Software “ be based on this ? See web site of Kurzweil . 

Using this can we “ re-write “ a resume ( lilke creating a step brother ), from a given “ Sample Resume “ ?


If such “ paraphrasing / re-writing “ of resumes can be done online ( automatically )  on our web site , then we could add one more element of FUN – even if the re-written resume contains some absurd text ! In fact , such “ absurdity “ may lend an element of FUN !  [  07  JAN  2007
  ]



Page  385

·         If we succeed in paraphrasing / re-writing a text resume online, we could also generate / develop a “ Subliminal / Subconscious “ function profile graph ! ( - since keywords would have got changed ) . This could be fantastic !



Page  388


·         Eg : Agents of Jobseekers negotiating Salary / Terms etc with Agents of Recruiters


·         Virtual Job Fair  will be / can be a “  Negotiating Platform “   for 
,
#  Buyers’ Agents  ( Recruiters )

#  Sellers’ Agents  ( Jobseekers )





Page  389

·         Only yesterday we discussed that in “ Post Jobs “ form ,recruiter will add,

    #  Put in my folder, all future/incoming resumes having percentile score of > xyz 


    Now he has created an AGENT which checks incoming resumes daily & puts into folder “ Resumes of Interest “ – also an email will go out to the recruiter concerned ( alert )



Page  440

·         A few weeks back , there was a report of a mechanical spider-legged robot , which learned on its own to change its “ gait “ when one of the legs was broken !



   
24  June  2017


www.hemenparekh.in / blogs            

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