1. Recruiting Process UsingA
Proposal Defense On
Simplifying
Recruiting Process Using
Information System
Presented By:
Dr.Gopal Thapa
Dhana Raj Chalise
Sindhu Madhav Dhakal
Krishna Joshi
Surya Bam
Nimesh Pokhrel
Deepika Bajracharya
Samir Sir
Sudip Raj Khadka

2. OUTLINE
Introduction Objectives Literature Review Methodology Sources of Data Sampling Data Instrument To Be Used Data Analysis Method Conclusion References

3. INTRODUCTION Human Resource Department
Importance of Recommender System
A job recommender system is expected to provide recommendations in 2 ways:
 recommending most eligible candidates for the specified job
 recommending jobs to the aspiring Candidates according to their matching profiles
Focus of this study is to recommend candidates according to their matching profiles

4. OBJECTIVES
To find the most relevance candidates for the job posted by recruiting site.
Ranking the relevance candidate with some threshold value K and recommending top K candidates to the company for further processing.

5. LITERATURE REVIEW RECOMMENDER SYSTEM:
The recommender system helps the user to find what they want within a very less time
TYPES OF RECOMMENDER SYSTEM:
Content Based
Collaborative
Knowledge Based
Demographics
Hybrid

6. LITERATURE REVIEW (CONTD….)
Authors Mourad Ykhlef, Shaha T. Al-Otaibi [7], have implemented the three techniques for ranking the candidates for the job postings as
Cosine similarity
Euclidean distance
New Jaccard measure
Machine learning algorithm has been used by Evanthia Faliagka et al. [1], to rank the job applicant using following model.

7. LITERATURE REVIEW (CONTD….)
Author Domenico Ursino et al. [4], has proposed a model that takes profiles of users, and representes in XML so that the intelligent model can recommend the relevance candidate.
Gediminas Adomavicius and Alexander Tuzhilin in [6] have defined TF-IDF vector for document representation and cosine similarity for semantic matching between documents.

8. METHODOLOGY
Figure : Conceptual model of proposed model for recommending candidate

9. METHODOLOGY (CONTD….)
Steps carried out for finding a relevance candidate
Job Offer Extraction
Document Preprocessing
Data Acquisition
Feature Selection
Domain Knowledge
 Document Representation

10. Document Preprocessing
METHODOLOGY (CONTD….)
Job Offer Extraction
Web Crawler
Parser
Document Preprocessing
Tokenization
Stop Word Removal
Synonym Expansion

11. Sources Of Data
Jobs Nepal ( ) Jaagire ( ) Mero Job ( Verisk Technology ( Deerwalk Technology ( Leapfrog Technology (

12. Feature Selection(Feature of candidate data)
Sampling
Feature Selection(Feature of candidate data)
S.N.
Feature
Categorization 1
Age
Three Groups ( It is assumed that candidate have been passed their graduate)
Group 1(21-30)A
Group 2(31-40)B
Group 3(>40)C 2
Gender
Two Groups
Group 1 (Male)M
Group 2 (Female)F 3
Martial Status
Three Groups
Group 1( Married)M
Group 2 (Unmarried)U
Group 3 (Divorced)D 4
Academic Qualification
Group 1 (Graduate)G
Group 2 (Master)M
Group 3(PHD or Above)P 5
Grading
Three Groups ( Here the marks is taken in Percentage)
Group 1 (80-100)D
Group 2(60-79)F
Group 3 (<60)P 6
Experience
Four Groups ( Here time is taken in year)
Group 1 (0)A
Group 2 (1-2)B
Group 3(3-5)C
Group 4 (>5)D

13. Features of Recruiter Data
Sampling
Features of Recruiter Data
S.N.
Feature
Categorization 1
Position
Three Groups(According to their level of importance)
Group 1(Highest)A
Group 2(Medium)B
Group 3(Initial like trainee or Interne)C 2
Pay Scale
Three Groups
Group 1 (>50K)H
Group 2 (10K-50K)M
Group 3 (<10K)L 3
Location
Two Groups
Group 1( Outside Valley))
Group 2 (Inside Valley)1 .

14. Data Instrument To Be used
An job applicant with features “Puja Dhakal, Nepalgunj, 25, Female, Single, B.E. (IT), 85%, 5 years” will be represented as
<A, F, U, G, D, C>

15. Data Analysis Method
Cosine Similarity Algorithm

16. Conclusion Fig Source management Information System
Managing The Digital Firm

17. REFERENCES:
Tzimas Giannis et al. (2012). Application of Machine Learning Algorithms to an Online Recruitment System. The Seventh International Conference on Internet and Web Applications and Services
Lama, Prabin. (2013). Clusteringsystem ontext mining using K-Means algorithm.Turku University of Applied Sciences
Norvag Kjetil, Oyri Randi. (2005).News Item Extraction for Text Mining in Web Newspapers, Department of Computer and Information Science.Norwegian University of Science and Technology

18. REFERENCES:
3.Ursino, Domenico. (2007). An XML-Based Multiagent System for SupportingOnline Recruitment Services. IEEE Transactions on Systems, MAN, and Cybernetics - Part A :- Systems and Human, VOL. 37, NO. 4
4. Adomavicius Gediminas , Tuzhilin Alexander. (2005). Toward the next generation of recommender systems: A survey of the State-of-the- Art and possible extensions. IEEE Transactions on knowledge and data engineering, VOL. 17, NO. 6

19. REFERENCES:
5. Adomavicius Gediminas , Tuzhilin Alexander. (2005). Toward the next generation of recommender systems: A survey of the State-of-the-Art and possible extensions. IEEE Transactions on knowledge and data engineering, VOL. 17, NO. 6
6. Mourad Ykhlef, Shaha T. Al-Otaibi. (2012). A survey of job recommender systems. International Journal of the Physical Sciences Vol. 7(29), pp

20. REFERENCES:
7. Diaby Mamadou et al. (2014). Field Selection for Job Categorization and Recommendation to Social Network Users. Advances in Social Networks Analysis and Mining, 2014 IEEE/ACM International Conferences on. 8.Evanthia Faliagka et al. (2012). Taxonomy Development and Its Impact on a Self-learning e- Recruitment System. Artificial Intelligence Applications and Innovations : 8th IFIP WG 12

21. REFERENCES:
9. Gupta Anika, D. Garg.(2014). Applying data mining for job recommendations by exploring job preferences. International Conferences on Advance in Computing, Communications and Informatics, ICACCI, IEEE, GCET 10. N. Bhte Avinash et. al. (2010). Intelligent Web Agent for Search Engines. International Conferences on Trends and Advances in Computation and Engineering, TRACE.

22. THANK YOU