Pinned Repositories
Automated-Job-Resume-Matching-Solution
According to a 2015 study on job seeking behavior by Pew Research Center, 79% of the job seekers utilized the online resources for their most recent employment (Aaron ,2015). This study result suggests that the online job boards become the major channel for job seekers in the digital era. However, another finding in the study indicates that most of the job seekers fail to match their experiences with the job requirements and spend hours on job board to apply job which is not seen to be suitable (Aaron, 2015). Additionally, Dr. John Sullivan conducted a similar research in 2013 which highlighted some interesting aspects: on average, 250 resumes are received for each job opening by the major organizations, more than 50% of the resumes does not meet the minimum requirement (John, 2013). This means the time our recruiter spends on these 50% of the resumes for each job is wasted. From both candidate and recruiter’s points of view, the phenomenon may suggest that the traditional online job board does not seem to simplify the job application process or reduce the effort required from both parties. With this challenge getting bigger and bigger, the demand to automate the resume - job matching process is getting increased as well. For instance, the content - based recommendation system (CBR) is introduced to analyze the job description to identify the potential area of interest to the job seekers (Shiqiang et al., 2016). To apply the concept in Singapore local context, our team has conducted a text mining project based on the data acquired from the major online job board in Singapore. The primary objective of this project is to create a machine learning model to accelerate the job - resume matching process. The detail of the text mining methodology and results are presented in the following sections.
axios
Promise based HTTP client for the browser and node.js
bionic-reading
A Chrome Extension for Bionic Reading on ANY website!
bookmarko
A site to manage your Bookmarks in a new way
Boostnote
A markdown editor for developers on Mac, Windows and Linux.
downshift
🏎 Primitive to build simple, flexible, WAI-ARIA compliant enhanced input React components
gcp-langchain
Example of running LangChain on Cloud Run
gobienan.github.io
My personal Website
my-turk
A tool driven approach to Mechanical Turk user experiments
vue-creator-preview
A preview component of the creator with more information on hover.
gobienan's Repositories
gobienan/my-turk
A tool driven approach to Mechanical Turk user experiments
gobienan/vue-creator-preview
A preview component of the creator with more information on hover.
gobienan/bookmarko
A site to manage your Bookmarks in a new way
gobienan/Automated-Job-Resume-Matching-Solution
According to a 2015 study on job seeking behavior by Pew Research Center, 79% of the job seekers utilized the online resources for their most recent employment (Aaron ,2015). This study result suggests that the online job boards become the major channel for job seekers in the digital era. However, another finding in the study indicates that most of the job seekers fail to match their experiences with the job requirements and spend hours on job board to apply job which is not seen to be suitable (Aaron, 2015). Additionally, Dr. John Sullivan conducted a similar research in 2013 which highlighted some interesting aspects: on average, 250 resumes are received for each job opening by the major organizations, more than 50% of the resumes does not meet the minimum requirement (John, 2013). This means the time our recruiter spends on these 50% of the resumes for each job is wasted. From both candidate and recruiter’s points of view, the phenomenon may suggest that the traditional online job board does not seem to simplify the job application process or reduce the effort required from both parties. With this challenge getting bigger and bigger, the demand to automate the resume - job matching process is getting increased as well. For instance, the content - based recommendation system (CBR) is introduced to analyze the job description to identify the potential area of interest to the job seekers (Shiqiang et al., 2016). To apply the concept in Singapore local context, our team has conducted a text mining project based on the data acquired from the major online job board in Singapore. The primary objective of this project is to create a machine learning model to accelerate the job - resume matching process. The detail of the text mining methodology and results are presented in the following sections.
gobienan/axios
Promise based HTTP client for the browser and node.js
gobienan/bionic-reading
A Chrome Extension for Bionic Reading on ANY website!
gobienan/Boostnote
A markdown editor for developers on Mac, Windows and Linux.
gobienan/downshift
🏎 Primitive to build simple, flexible, WAI-ARIA compliant enhanced input React components
gobienan/gcp-langchain
Example of running LangChain on Cloud Run
gobienan/gobienan.github.io
My personal Website
gobienan/JavaScript-Templates
1KB lightweight, fast & powerful JavaScript templating engine with zero dependencies. Compatible with server-side environments like node.js, module loaders like RequireJS and all web browsers.
gobienan/monaco-themes
Themes to be used and generated with monaco-editor in web browser
gobienan/pressTheNumber
gobienan/pressTheNumber.com
gobienan/uiui
Created with CodeSandbox
gobienan/vue-textarea-autosize
Vue component provides textarea with automatically adjustable height and without any wrappers and dependencies
gobienan/vue-toasted
🖖 Responsive Touch Compatible Toast plugin for VueJS 2+
gobienan/vuejs-medium-editor
A medium like text editor for vue js WYSIWYG
gobienan/webextensions-examples
Example Firefox add-ons created using the WebExtensions API
gobienan/webrtc-firebase-demo
Video Chat with WebRTC and Firebase