The document discusses data-intensive bidirectional matching of job seekers to vacancies. It motivates the need for improved matching by noting that jobs often go unfilled due to a lack of suitable applicants or applicants providing incomplete information. The document then outlines methods for web mining job descriptions and resumes, using natural language processing to analyze the text, and machine learning to extract patterns and present matching results. It evaluates different data sources and preprocessing techniques before matching job seekers and vacancies based on document similarity analysis and clustering.

1. Data-intensive Bidirectional Matching of
Job Seeker to Vacancy
Sisay Adugna Chala
Institute of Knowledge Based Systems and Knowledge Management
University of Siegen
sisay.chala@uni-siegen.de
June 26, 2017
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2. Sisay Adugna Chala (University of Siegen) EDUWORKS 2017 June 26, 2017 2 / 20

3. Motivation
Jobs are not filled because of lack of the right applicant 1
Job seekers don’t have access to the right jobs
Vacancies are not complete
Applicants do not have complete information, e.g., preferences
Jobs are not filled because of applicants quitting, being fired 2
Applicants under- or overstated their suitability to the job
1 (Belloni, M., Brugiavini, A., Meschi, E., and Tijdens, K. G., 2016)
2 (Branham, L., 2012)
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4. Motivation ...
Huge online data of job descriptions entered by job seekers and
job holders
Employers produce large volume of vacancy data online
Due to this volume, not all vacancies are reachable by job-seekers
who have relevant skill set
Not all vacancies specify all the required skills
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5. Motivation ...
Sisay Adugna Chala (University of Siegen) EDUWORKS 2017 June 26, 2017 5 / 20

6. Matching
Why is matching of job seeker to vacancy is challenging as
compared to other matching problems?
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7. Bidirectional Matching
Definition
Bidirectional Matching measures
the degree of semantic similarity of
job seekers against vacancies and
matches one to the other
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8. Methods and Tools
Web mining – to scrap online vacancy and resumé data
Natural language processing – to represent the textual data
Machine learning – to extract, and present the analysis result
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9. Data Source
Wageindicator dataset
Online job description data
from occupational standards
Online vacancy data via
crawling
Online resumé
Social networking data
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10. Data Pre-processing
Sisay Adugna Chala (University of Siegen) EDUWORKS 2017 June 26, 2017 10 / 20

11. Document Similarity Analysis and Clustering
Building a document vector in order to represent the document as
a whole using statistically most important words contained in
the document
Similarity analysis applications work by comparing the vectors of
documents using Latent Semantic Analysis (LSA)
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12. Baseline
c000 Software Developer Intern
c001 Business Development Manager IOT
c002 Web Developer
v000 Software Development and Integration Manager
v001 Software Development Manager, IT integration
v002 Industrial Internet of Things Manager
v003 IoT Software Engineer, v004 IoT Saas Architect
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13. Baseline
c000 Software Developer Intern
c001 Business Development Manager IOT
c002 Web Developer
v000 Software Development and Integration Manager
v001 Software Development Manager, IT integration
v002 Industrial Internet of Things Manager
v003 IoT Software Engineer, v004 IoT Saas Architect
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14. Context-aware Dynamic Text Field
Context-aware Dynamic Text Field (DTF) for input
recommendation 3
Improving collection of job seeker info via web survey
Methods Used
String distance
Co-occurrence probability of words
3 (S. Chala, F. Ansai, & M. Fathi, 2016)
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15. Context-aware Dynamic Text Field
Added Values
Adaptive to context
Ergonomically suitable
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16. Integrating Social Network Data
Enriching job seeker with social networking data 4
4 (S. Chala and M. Fathi, 2017)
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17. Integrating Social Network Data
ID Reputation Description Up
vote
Down
vote
89 1173 Network,
Engineer,
Service,
Provider,
Networks,
IPv6,
Network,
Security
6 0
Sisay Adugna Chala (University of Siegen) EDUWORKS 2017 June 26, 2017 16 / 20

18. Integrating Social Network Data
ID Reputation Description Up
vote
Down
vote
89 1173 Network,
Engineer,
Service,
Provider,
Networks,
IPv6,
Network,
Security
6 0
network engineer service provider ipv6 security
3 1 1 1 1 1
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19. Result
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20. Conclusion
Unsupervised feature learning from vacancies and job seekers
Improved DTF user interface for data collection
Social networking data enhanced job seeker
Improves precision of matching
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21. Future Work
Extract the required and desired skills when a vacancy has not
explicitly categorized them
Adding multilingual capability to support seamless labor force
mobility
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22. Thank You
Thank You
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