1. Final Year Project – I Smart Recruiter Group Members: Uzair Siddiqui [05363] Rehma Ather [05625] Meeran Khan [05364] Syed Maaz Alam [05284] Supervisor - Sir. Shams Naveed Zia

2. Task division Database design, setup and back-end algorithms: Meeran Khan & Maaz Alam Most of the work is concerned with Neo4j and Python Front-end design, wireframes, UX/UI: Uzair Siddiqui & Rehma Ather Most of the work is concerned with AngularJS, HTML, CSS Wireframes were created using a tool named ‘wireframesketcher’

3. Problem Introduction Shortlisting job applicants for interviews Current approach HR team manually scans CVs of candidates Drawbacks Hectic Costs time

4. Project Overview Our approach Make the machine do it HR makes job specific application forms Applicants fill forms Recommendation scores calculated for candidates Scoring metrics used HR gets list of candidates ordered by score Most suitable at top

5. Project Overview [Cont.] Scoring: HR Creates form [or picks template] Provides job requirements [scoring criteria] Candidates Fill form Data compared with aforementioned criteria Scored based on similarity [more similar, higher score]

6. Prototype – Home Screen

7. Job requirement form(to be filled by the recruiter)

8. Job application form (to be filled by the candidate)

9. Applicants list with score

10. Technologies Used Front-end: HTML CSS Angular JS Back-end: Neo4j [graph database] Python [data fetching, server side calculations]

11. This is not a Job Portal Unlike rozee.pk or naukri.com, this is not an application that helps connect recruiters with potential candidates. This is an application specifically developed to ease the cumbersome task of shortlisting different applicants once they have applied for a job. This is basically a recommender system that provides recruiters with suitable recommendations so that they don’t have to scan CVs or form data submitted by the applicant

12. Comparison with similar Applications – Rozee.pk Rozee.pk doesn’t provide any mechanism for recruiters to find candidates based specifically on their job requirement and then rank them based on their relevancy to the job requirement. Rozee.pk does have a CV search engine, but it is mainly based on keyword searches which is exactly the same as what many Applicant Tracking Systems do. It is not always a true reflection of the best candidate. Just like document searches. Rozee.pk mainly relies on filters. It provides filters such as location filter, industry filter etc.

13. Comparison with similar Applications – Naukri.com Naukri.com has a CV score calculator, but it doesn’t analyze the CV’s content and generate recommendations based on that analysis. What it provides is mainly a structural analysis of the CV.

14. Comparison with similar Applications – Naukri.com – 1/4

15. Comparison with similar Applications – Naukri.com – 2/4 Resume Score

16. Comparison with similar Applications – Naukri.com – 3/4 Resume Score- Continued

17. Comparison with similar Applications – Naukri.com – 4/4 Format Type of bullet points seem consistent throughout your resume More than 20% resume is in bold. Bolding should be used to highlight important content only Line spacing throughout your resume is not consistent Work experience is not listed in reverse chronological order. It is suggested that you have your latest experience mentioned first Content Targeted functional area or industry is appropriately mentioned in the objective, summary or personal details section Your resume has a lot of spelling errors, Requires urgent correction Your resume does not contain repeated facts providing better impact Some example points from the above report

18. Comparison with similar Applications – Linkedin.com – 1/2 LinkedIn has a whole separate service offering by the name LinkedIn Recruiter. LinkedIn recruiter provides its users with a lot of custom filters in order to narrow down on the right candidate. Filters may include: Status Location Industry School Degree Past company Company size Seniority Level Years of Experience and many more.

19. Comparison with similar Applications – Linkedin.com – 2/2 These filters although do provide some level of relevancy, but when narrowing down on candidates, it doesn’t provide any functionality to rate the profiles based on the job requirement. For example, a company needs a software developer who has 5+ years of experience working on AngularJS. In that case, LinkedIn can find people based on the fact that they have listed AngularJS in their skills section and they have a total of 5+ years of work experience, but for recruiter there is no way of finding out how many years of experience the developer has working specifically on AngularJS. The recruiter would have to see the candidates LinkedIn profile to understand in what capacity the person has worked in the past.

20. Major Feature of Smart Recruiter One service that no company is giving is real-time rating of different CVs given the job description. Companies today have a data bank in which they store a lot of CVs. These are the CVs that people send when they are job hunting, usually not because of a particular job opening. Whenever there is a new job opening, HR employees scan their CV bank in the hopes of finding a good match. Smart recruiter will use text analytics to analyze the job description and all the corresponding CVs the recruiter will provide. And apply Bayesian statistics to rate CVs according to the provided job description.

21. Summing it all Up – FYP 1 We have created an online application which recruiters can use to create recruitment specific forms in which they can set their requirements according to the job description. Recruiters can then publish these forms on the internet using a public link for the form. Interested candidates can then apply for the job using these forms. Once a candidate applies using the form, a recommendation score is generated based on his/her data. The recruiter can compare and contrast different candidates based on their recommendation scores.

22. Plans for FYP – 2 Alternative candidate application methods Apply through LinkedIn Upload CV Improving Recommendation Use Bayesian statistical methods Integrate overall collected data Recommendation based on overall knowledge of field Expand to other fields Business Engineering Health, etc.

23. Thank You