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Job Seeker

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Mange Chen ([email protected]) Department of Computer Science, Indiana University, Bloomington, IN USA Job Seeker Introduction The planner of Job Seeker is to build a business and employment-oriented database that operates via external party developer API such as Wikipedia. Design Strategy Conclusion & Future Work References Acknowledge [1]. Lin, Yiou, Hang Lei, Prince Clement Addo, and Xiaoyu Li. Machine Learned Resume-Job Matching Solution. Computation and Language. ArXiv.org, 26 July 2016. Web. [2]. Hirst, Tony. "OUseful.Info, the Blog..." OUsefulInfo the Blog. Https://blog.ouseful.info/, 30 Apr. 2016. Web. 05 Dec. 2016. Results & Analysis Implementation Objective The basic functionality of Job Seeker allows (workers and employers) to find the suitable job or applicants by searching key words of skills. •Users can observe the degree of correlation through a chart of skills. •Users can find jobs, people and business opportunities by searching key words of their skills such as Ruby. •Employers can find the most qualified applicants. Job Seeker Step One: Use Python to crawl skills data (pages, subcategories, categories). Step Two: Use NetworkX package in Anaconda to generate graphs. These solutions are typically driven by manual search-based rules and pre- defined keyword weights, which results in an inefficient and frustrating search experience. The job of searching through online matching engines is now very prominent and is beneficial to job seekers and employers to extract information directly from resumes and vacancies. By search single key word of skills, we can find out other skills related to it. This information provides chance for workers to increase their competitiveness. This work is supported by the Undergraduate Research of Computing (UROC) in Indiana University Bloomington. Thank Mohsen Sayyadiharikandeh for comments that greatly improved the manuscript, and Cassidy Wichowsky for sharing her pearls of wisdom with me during the research. Figure 2. This is a example of the sort of thing we can get out for a search seeded on skills associated with Computer Science such as Ruby. The to-do list is automated by adding some network statistics to the NetworkX step and possibly a first pass layout. Figure 1. The simple connection between pages, subcategories and categories. 1 is the main category contains the subcategories 2 and 0. Both 2 and 0 are subcategories. 3 is the page which belongs to subcategory named 2. In this poster, I have considered the matching problem of skills and proposed a solution by using ensemble methods. In the future, through the website to update more information, job seekers solutions can be extended by including location information, professional skills and requirements to expand. Figure 4. By way of demonstrating how the recipe described in Visualizing Related Entries in Wikipedia Using Gephi can easily be turned to other things, here’s a map of how different computer programming languages influence each other according to DBpedia/Wikipedia. [2] Figure 3. This graph shows a large image example of Computer Science connections when we search “Java”. By looking at the graph, we can easily find out which language skill is the most popular for recruitment and which skill has strong connections with others.
Transcript
Page 1: Job Seeker

Mange Chen ([email protected])Department of Computer Science, Indiana University, Bloomington, IN USA

Job Seeker

Introduction

The planner of Job Seeker is to build a business and employment-oriented database that operates via external party developer API such as Wikipedia.

Design Strategy Conclusion & Future Work

References

Acknowledge

[1]. Lin, Yiou, Hang Lei, Prince Clement Addo, and Xiaoyu Li. Machine Learned Resume-Job Matching Solution. Computation and Language. ArXiv.org, 26 July 2016. Web.[2]. Hirst, Tony. "OUseful.Info, the Blog..." OUsefulInfo the Blog. Https://blog.ouseful.info/, 30 Apr. 2016. Web. 05 Dec. 2016.

Results & Analysis

Implementation

ObjectiveThe basic functionality of Job Seeker allows (workers and employers) to find the suitable job or applicants by searching key words of skills.•Users can observe the degree of correlation through a chart of skills.•Users can find jobs, people and business opportunities by searching key words of their skills such as Ruby.•Employers can find the most qualified applicants.

Job Seeker

Step One: Use Python to crawl skills data (pages, subcategories, categories).Step Two: Use NetworkX package in Anaconda to generate graphs.

• These solutions are typically driven by manual search-based rules and pre-defined keyword weights, which results in an inefficient and frustrating search experience.

• The job of searching through online matching engines is now very prominent and is beneficial to job seekers and employers to extract information directly from resumes and vacancies.

• By search single key word of skills, we can find out other skills related to it. This information provides chance for workers to increase their competitiveness.

This work is supported by the Undergraduate Research of Computing (UROC) in Indiana University Bloomington.Thank Mohsen Sayyadiharikandeh for comments that greatly improved the manuscript, and Cassidy Wichowsky for sharing her pearls of wisdom with me during the research.

Figure 2. This is a example of the sort of thing we can get out for a search seeded on skills associated with Computer Science such as Ruby.The to-do list is automated by adding some network statistics to the NetworkX step and possibly a first pass layout.

Figure 1. The simple connection between pages, subcategories and categories. 1 is the main category contains the subcategories 2 and 0. Both 2 and 0 are subcategories. 3 is the page which belongs to subcategory named 2.

• In this poster, I have considered the matching problem of skills and proposed a solution by using ensemble methods.

• In the future, through the website to update more information, job seekers solutions can be extended by including location information, professional skills and requirements to expand.

Figure 4. By way of demonstrating how the recipe described in Visualizing Related Entries in Wikipedia Using Gephi can easily be turned to other things, here’s a map of how different computer programming languages influence each other according to DBpedia/Wikipedia. [2]

Figure 3. This graph shows a large image example of Computer Science connections when we search “Java”.By looking at the graph, we can easily find out which language skill is the most popular for recruitment and which skill has strong connections with others.

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