Machine Learning driven Job Recommender System

2021 COE Engineering Design Project (AA10)

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Faculty Lab Coordinator

Alagan Anpalagan

Topic Category

Software Systems

Preamble

Recommender systems (RS) that help users discover new products and services. RS are like salesman who knows, based on the history and preferences, what someone likes and likely to make a purchase. Based on your resume (skill sets, education and experience etc). RS can recommend jobs but they have to be matched by the job openings posted by employers. In this project, an intelligent and accurate recommender system will be developed to recommend most suitable jobs in most suitable companies based on input from text-based resume or integrated with professional network such as LinkedIn or other forms.

Objective

The overall goal is to design, develop, implement and test an integrated recommender system to match job seekers to job postings. Different kind of data will be collected and analyzed to make a decision accurately as possible using data processing and machine learning techniques. It should be done cost effective manner possible without compromising the quality of the product and services to be provided by the system.

Partial Specifications

The partial specs listed: The integrated recommender system to be implemented should: (a) use of web and mobile application, (b) require data collection, storage/processing, analysis/recommending using machine learning technologies, (c) involve real-time and non-real-time data collection from sources such as resumes, LinkedIn, job posting websites, (d) use Python or Java/C++, and Android/iPhone-based display, (e) demonstrate efficiency in automated operations for end users. This project may be done with the combination of simulation and real implementation.

Suggested Approach

- Study the literature/technical papers on recommendation system, data acquisition/processing, machine learning techniques
- Design data collection and processing system (using social networks as appropriate) and implement in cloud-based environment
- Incorporate intelligent decision making using machine learning technologies to process available data from job seekers and employers) to make recommendations
- Use modular approach in design/test and then integrate.
- Design/develop the GUI with appropriate inputs and ease of use
- Test the integrated system for typical/extreme utility conditions which will be done through simulation and some implementation.

Group Responsibilities

Study recommender systems in general and in job market in particular, design/develop the technical specifications required for the system prototype under consideration, implement and test of the entire system with the above objectives. Follow the project management plan carefully and thoroughly. Responsible for the demo and project report.

Student A Responsibilities

To design, develop and implement a robust recommender system for job seekers and hiring employers based on machine learning techniques.

Student B Responsibilities

To design, develop and implement a robust recommender system for job seekers and hiring employers based on machine learning techniques.

Student C Responsibilities

To design, develop and implement a robust recommender system for job seekers and hiring employers based on machine learning techniques.

Student D Responsibilities

To design, develop and implement a robust recommender system for job seekers and hiring employers based on machine learning techniques.

Course Co-requisites

To ALL EDP Students

Due to COVID-19 pandemic, in the event University is not open for in-class/in-lab activities during the Winter term, your EDP topic specifications, requirements, implementations, and assessment methods will be adjusted by your FLCs at their discretion.