Job Recommendation System Research Articles

Designing an Effective Job Recommender System based on Embedded Machine Learning Models

The need to automate employment offers to qualify job searchers has gain attentions. For an automated recommendation systems to be used more frequently, a better user-friendly filtering techniques are required. This paper designs an automated process, referred to as “the job recommender”, which focuses on user-centric design and personalization for recommending and matching applicants with appropriate jobs. We use the bottom-up approach that uses dataset based on filtering algorithms to predict and make recommendations for job seekers. The algorithm helps the recruiters to produce the list of the résumé that best meets the job descriptions. In this context, the random forest (RF) and support vector machines (SVM) are adopted to train the data. They are supplied personalized information (qualifications, result of aptitude test, age, and work experience) reported on the résumés of individual candidates from the pool of submissions, and the system train data to learn the evolution of job selection by candidates based on these machine learning tools. The algorithm used would help the recruiters to produce the list of the résumé that best meets the job descriptions. The algorithms are designed to recommend personalized items tailored to each user's interests. Under the minimum hardware and software requirements, the job recommender system was implemented in streamlit - a python template, for designing the frontend.

Balancing Accuracy and Efficiency: A Comparative Analysis of Collaborative Filtering Algorithms for Job Recommendation Systems

Balancing Accuracy and Efficiency: A Comparative Analysis of Collaborative Filtering Algorithms for Job Recommendation Systems

Navigating techniques in job recommender systems on internship profile matching: a systematic review

PurposeThis review article is focused on the following research questions: RQ1: What are the methods used by authors to collect data in order to evaluate one's profile? RQ2: What are the classification algorithms and ranking metrics used to give suggestions to users? RQ3: How effective are these algorithms and metrics identified in RQ2?Design/methodology/approachThere are four major systematic review phases being carried out in this survey, namely the formulation of research questions, conducting the review, which includes the selection of articles and appraising evidence quality, data extraction and narrative data synthesis.FindingsCollecting from primary sources is more personalized and relevant. Embedded skill sets that have a considerable impact on one’s career aspirations could be mined from secondary sources. A hybrid recommender system helped mitigate the limitations of both. The effectiveness of the models depends not only rely on the filtering techniques used but also on the metrics used to measure similarity and the frequency of words or phrases used in a document.Research limitations/implicationsThe study benefits internship program coordinators of a university aiming to develop a recommender or matching system platform for their students. The content of the study may shed a light on how university decision-makers can explore options on what are the techniques or algorithms to be integrated. One of the advantages of internship or industrial training programs is that they would help students align them with their career goals. Research studies have discussed other RS filtering techniques apart from the three major filtering techniques.Practical implicationsThe outcome of the study, which is a recommendation system to match a student's profile with the knowledge and skills being sought by organizations, may help ease the challenges encountered by both parties. The study benefits internship coordinators of a university who are planning to create a recommendation system, an innovative project to be used in teaching and learning.Social implicationsInternship programs can help a student grow personally and professionally. A university student looking for internship opportunities can find it a daunting task to undertake, as there is a vast pool of opportunities offered in the market. The confidence levels needed to match their knowledge, skills and career goals with the job descriptions (JDs) could be challenging. The same holds with companies, as finding the right people for the right job is a tough endeavor. The main objective of conducting this study is to identify models implemented in recommendation systems to give and/or rank suggestions given to users.Originality/valueWhile surveys regarding recommender systems (RS) exist, there are gaps in the presentation of various data collection methods and the comparison of recommendation filtering techniques used for both primary and secondary sources of data. Most recommendation systems for internship programs are intended for European universities and not much for Southeast Asia. There are also a limited number of comparative studies or systematic review articles related to recommendation systems for internship programs offered in an Southeast Asian landscape. Systematic reviews on the usability of the proposed recommendation systems are also limited. The study presents reviews of articles, from data collection and techniques used to the usability of the proposed recommendation systems, which were presented in the articles being studied.

Enhancing Job Recommendation Systems through Machine Learning: A Comprehensive Analysis of Skill Sync Job Recommendation System

Enhancing Job Recommendation Systems through Machine Learning: A Comprehensive Analysis of Skill Sync Job Recommendation System

Dynamic Landscape of Higher Education, The Intersection of Student Skills and Industry Demand

In today's dynamic job market, higher education students are confronted with the challenge of sifting through numerous career options that align with their skills, interests, and educational background. To tackle this hurdle, "Dynamic Landscape Of Higher Education, The Intersection Of Students Skills And Industry Demand" introduces a personalized job recommendation system tailored explicitly for higher education students. This system utilizes advanced recommendation algorithms and data analysis techniques to provide students with targeted job suggestions based on their academic qualifications, skills, interests, and career preferences. With a user-friendly interface, students can effortlessly input their information and receive tailored recommendations that evolve over time through continuous learning from user feedback. By offering data-driven insights and facilitating informed career decisions, this system empowers students to explore diverse career pathways and seamlessly transition from education to employment. The user interface of this system is designed to be intuitive and user-friendly, enabling students to easily input their information and preferences to receive tailored job recommendations. The recommendation system is adaptive, continuously learning from user feedback and interaction, ensuring that the recommendations provided evolve over time to better meet the changing needs and preferences of students. Recognizing the widening gap between industry requirements and graduating students' skills, this project proposes a comprehensive job recommendation system to bridge this divide and enhance placement opportunities. Leveraging advanced algorithms and machine learning techniques, the system evaluates students' skill sets and matches them with relevant job roles, facilitating a smooth transition from academia to the workforce. This multifaceted approach includes data collection, algorithm development, user interface design, and integration with existing campus placement systems. Collaborative efforts with academic institutions and industry partners aim to provide personalized job recommendations tailored to individual students' abilities and aspirations, with a focus on fairness and equity. Key components of the project include the design and implementation of an intuitive user interface, data-driven skill assessment methodologies, and continuous refinement of the recommendation algorithm based on user feedback and industry insights. Ethical considerations regarding data privacy and security are carefully addressed, reflecting a commitment to responsible use of student data. Ultimately, the success of the job recommendation system is measured by its ability to match students with suitable employment opportunities and its broader impact on career readiness and long-term professional success. By empowering students with the tools and resources needed to navigate the complexities of the modern job market, this project aims to catalyze positive change and foster a more inclusive and equitable future for all stakeholders involved.

CareerBoost: Revolutionizing the Job Search with Resume Enhancement and Tailored Recommendations

The Resume Enhancer and Job Recommendation System is designed to meet the unique challenges faced by job seekers in today's dynamic job market. Leveraging cutting-edge natural language processing (NLP) techniques, the present system provides a tailored solution to streamline the job search process. The present Resume Enhancer component utilizes advanced NLP algorithms to analyse resumes and job descriptions, generating comprehensive eligibility scores and targeted skill recommendations. This ensures that candidates' resumes are optimized to effectively showcase their qualifications and expertise to potential employers. The present Job Recommendation feature delivers personalized job listings tailored to each user's selected roles or career aspirations. The authors implemented machine learning algorithms such as the Random Forest Classifier and K-means Clustering, the system matches candidate preferences and qualifications with relevant job opportunities, increasing the likelihood of finding the perfect fit. Overall, the Resume Enhancer and Job Recommendation System serves as a valuable tool for job seekers, empowering them to navigate the complexities of the modern job market with confidence. With its user-centric approach and advanced technology, the present system enhances employability and facilitates career growth for individuals at every stage of their professional journey.

A Review on Job Recommendation System

With the rapid growth of artificial intelligence and machine learning technologies, the recommendation system aims to help users find items that match their preferences. In order to improve performance, many recommendation system techniques have been proposed. This paper presents a survey of some common recommendation techniques and related issues with advantages and disadvantages. At the same time, the different types of job recommendation systems are described in detail and compared with each other. The goal is to provide a comprehensive overview of the current state of job recommendation systems and to analyze the characteristics of each system. The results of the case studies can contribute to a better understanding of the strengths and weaknesses, as well as techniques used in different job recommendation systems. Through this review, insights are provided to guide the development of more effective recommendation systems. For future research, a system is proposed to generate career move recommendations with upskilling and reskilling suggestions.

Advancing Job Search: A Comprehensive Resume-Based Job Recommendation System Using NLP and Deep Learning Techniques

Abstract: Students find it difficult to find jobs and postings related to their skills and which has a good pay. Students aimlessly scan all internet to find a job which best matches their skill. Hence this paper presents a model which would give them personalized recommendations based on their skills mentioned in the resume. In today’s world hundreds of people apply for a single job posting. Companies receive thousands of applications at any given time. To save time companies, recruiters have few seconds to go through the applicant’s resume. Moreover, today all companies use a software based application which scans all resumes and get the best candidates. Most students especially freshers fail at this critical juncture, they fail to make a good resume which would help their resumes getting selected. Our novel model helps solve this problem. In order to extract sectionspecific text content from resumes, this work presents a layout-aware resume parsing system based on natural language processing (NLP) and rule-based approaches. This output can be fed into a resume content review system to obtain resume feedback, or it can be utilized as the input for a resume content score model. By utilizing sophisticated natural language processing (NLP) models, the system guarantees precise recognition and classification of essential resume elements, including personal information, qualifications, experience, and accomplishments. The recommendation engine makes use of machine learning techniques to pinpoint areas that need work, such improving keyword relevancy, recommending extra abilities, or offering formatting and wording advice. The goal of this iterative process is to provide job seekers with dynamic tools that will boost their visibility to recruiters and applicant tracking systems. Our unique model also recommends skills to add in their resume. This models also recommends some add-on courses to improve those skills. The app also uses cutting-edge technology like Natural Language Processing (NLP) and Optical Character Recognition (OCR) to process resumes and job listings and find the greatest fit for both job seekers.

Measuring Bias in Job Recommender Systems: Auditing the Algorithms

Measuring Bias in Job Recommender Systems: Auditing the Algorithms

Measuring Bias in Job Recommender Systems: Auditing the Algorithms

Measuring Bias in Job Recommender Systems: Auditing the Algorithms

Forward Chaining Algorithm on Informatics Graduate Job Recommendation System Based on MBTI Test

The Myers-Briggs Type Indicator (MBTI) is a method for identifying an individual's personality type based on the psychological theory of Carl Gustav Jung. In the context of computer science students, they often face challenges in planning their academic journey and determining the direction of their career development during their studies, causing confusion when it comes to choosing a career path in the field of computer science in the future. To address these challenges, the researcher has developed a web-based expert system using the PHP programming language. This expert system is designed to make decisions based on a collection of user responses, which are processed using the forward chaining method, ultimately providing the user's personality type along with suitable career choices. The primary objective of the expert system is to assist students in making decisions regarding their studies and future careers. Through this research, the researcher has produced a functioning website capable of efficiently processing user responses and generating decisions regarding personality types and career options. Thus, this study provides a solution to aid computer science students in planning their academic and career paths.

Job Recommendation System Using Deep Reinforcement Learning (DRL)

The rapid growth of online job portals and the increasing volume of job listings have made it challenging for job seekers to efficiently navigate through the vast number of available opportunities. Job recommendation systems play a crucial role in assisting users in finding relevant job opportunities based on their skills, preferences, and past experiences. This research paper proposes a job recommendation system that leverages deep learning techniques to enhance the accuracy and effectiveness of job recommendations. The system utilizes advanced algorithms to analyses user profiles, job descriptions, and historical data to generate personalized job recommendations. Experimental evaluations demonstrate the superiority of the proposed system compared to traditional recommendation methods, thereby improving the job search process for both job seekers and employers. This paper provides Job recommendation system using Deep Reinforcement Learning (DRL).

Job Recommendation for Daily Paid All Workers

stract: In the last years, job recommender systems have become popular since they successfully reduce information overload by generating personalized job recommendation. Currently recommendation Systems are utilized to handle the issue of the overwhelming amount of data or the information in every domain and enables the clients to concentrate on data that is more relevant to their area of interest. One such field where recommender frameworks can play a vital role is to help workers who works on daily wages basis by recommending a job based on their skills and interest. In the current scenario, with an abundance of different industries and fields, a huge number of jobs are available for the skilled and literate professionals. It is not difficult to find suitable jobs for a person after his field has been identified but the main obstacle for achieving this goal is lack of information and awareness. The problem is that there is no such relevant recommendation system available currently so, we proposed the “Job recommendation system for daily paid workers” by analysing the skills of a particular worker and then finding appropriate jobs in his area of interest. To make this system even more robust, a wide variety of factors are taken into consideration while recommending jobs to a worker’s who work on daily wages basis

Job Recommendation System Using NLP

Abstract: Recommender systems is one of the most successful machine learning applications. We are exposed to the products/information recommended by these systems everywhere in our daily life. This project will introduce several recommender systems in NLP, specifically in the domain of online recruitment. I will explain the important ideas behind the recommender systems in our web application, it might be also similar with those in recruiting platforms like LinkedIn, Xing. One of my main jobs is to design and develop recommender systems on the skill-based matching app. Before getting into this specific topic, I would like to go over the general concepts and methods of current recommender system in different domains. We have provided a brief overview of the approaches, techniques, and applications of recommendation systems in this paper. One significant application of recommendation systems is in the field of job recruitment, where candidates are chosen using online job recruitment portals based on their profiles, employment histories, and behavioral tendencies. According to a recent survey, this field has not been thoroughly investigated up to this point, and the current job recommender system has many flaws. They analyze resumes, profiles, and job descriptions, but because of the "cold start" problem, new job postings and candidate profiles are not properlymatched. In some cases, potential candidates lose their jobs as a result of inadequate job descriptions and education information in the ontology.

Job Recommendation System for Daily Paid Workers using Machine Learning

Abstract: In developing countries where unskilled workers often face challenges in finding suitable employment, this is where recommendation systems come in, as they can help users find information that is specific to their interests. Using the system decision-making and predictions through algorithms trained on available data across multiple domains makes it easy for users to access pertinent information. One area where recommendation systems can have a significant impact is in helping unskilled workers find jobs based on their skills and interests. Although there are many jobs available for skilled professionals, it can be challenging for daily wage workers to find suitable employment due to a lack of information and awareness. Currently, there are no relevant recommendation systems available to help these workers. In this research, we propose a "Job recommendation system for daily paid workers using Machine Learning" that analyzes a worker’s skills and interests to find appropriate job opportunities. To ensure that the system is robust, we consider a wide range of factors when recommending jobs to daily wage workers.

Job Recommendation Systems: A Literature Review

This paper comparatively reviews the different recommendation models amidst the different classifier filtering techniques. Recommender systems have been adopted in other areas of life to provide custom-made solutions to netizen. The accuracy or weakness of a recommender system is based on the recommendation approach and filtering techniques adopted. Recommenders’ systems are developed to suit the needs and personal interests of the users. This paper review looks into previous and current recommender systems with a view of the different characteristics, advantages and disadvantages. This paper also undertakes a comparative review of hybrid recommendation techniques as regards to improving job recommendation systems.

AI4HR Recruiter: a job recommender system for internal recruitment in consulting companies

Recruiting job candidates is a crucial activity for consulting companies to fulfill their consulting activities for customers or internal projects. Due to the complexity of the recruitment process, companies adopt standardized resume formats to be able to identify or compare candidates. However, this process requires significant involvement from human resource specialists and project leaders. In this paper, we present AI4HR Recruiter, a recommender system designed to improve the internal recruitment process of consultants. The system uses artificial intelligence-based techniques, specifically pre-trained sentence transformers. It aims at supporting recruiters in identifying and selecting candidates, rather than automating the process. We measure improvement in efficiency in terms of gains in recruiters time induced by the recommendation explanation presence. We also measure the impact on users’ satisfaction of two different types of algorithm to generate recommendations. To do so, we have been conducting a two fold study in a real-life setting for six months, where on the one hand, telemetry data was gathered while recommendation process user interaction was being performed and on the other hand, surveys about users’ satisfaction were conducted afterward. Our results evaluation demonstrates the effectiveness of the system in assisting recruiters in candidates selection process.

Learning-Based Matched Representation System for Job Recommendation

Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.

Technical Job Recommendation System Using APIs and Web Crawling.

There has been a sudden boom in the technical industry and an increase in the number of good startups. Keeping track of various appropriate job openings in top industry names has become increasingly troublesome. This leads to deadlines and hence important opportunities being missed. Through this research paper, the aim is to automate this process to eliminate this problem. To achieve this, Puppeteer and Representational State Transfer (REST) APIs for web crawling have been used. A hybrid system of Content-Based Filtering and Collaborative Filtering is implemented to recommend these jobs. The intention is to aggregate and recommend appropriate jobs to job seekers, especially in the engineering domain. The entire process of accessing numerous company websites hoping to find a relevant job opening listed on their career portals is simplified. The proposed recommendation system is tested on an array of test cases with a fully functioning user interface in the form of a web application. It has shown satisfactory results, outperforming the existing systems. It thus testifies to the agenda of quality over quantity.

Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration

With millions of students/employees browsing course information and job postings every day, the need for accurate, effective, meaningful, and transparent course and job recommender systems is more evident than ever. The current recommendation research has attracted wide attention in the academic and industrial areas. However, existing studies primarily focus on content analysis and user feature extraction of courses or jobs and fail to investigate the problem of cross-domain data integration between career and education. At the same time, it also fails to fully utilize the relations between courses, skills, and jobs, which helps to improve the accuracy of the recommendation. Therefore, this study aims to propose a novel cross-domain recommendation model that can help students/employees search for suitable courses and jobs. Employing a heterogeneous graph and community detection algorithm, this study presents the Graph-Community-Enabled (GCE) model that merges course profiles and recruiting information data. Specifically, to address the skill difference between occupation and curriculum, the skill community calculated by the community detection algorithm is used to connect curriculum and job information. Then, the innovative heterogeneous graph approach and the random walk algorithm enable cross-domain information recommendation. The proposed model is evaluated on real job datasets from recruitment websites and the course datasets from MOOCs and higher education. Experiments show that the model is obviously superior to the classical baselines. The approach described can be replicated in a variety of education/career situations.