Job Recommendation for Daily Paid All Workers

Abstract

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.

Introduction

I. INTRODUCTION

Machine learning is a branch of Artificial Intelligence (AI) which is used for decision making or predictions by training the available data with help of algorithms. 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 unskilled 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. The workers will apply for jobs available; contractor will check the details of workers and will accept or reject after verification. In job recommendation module only, job is predicted to job seeker. Only personal details about the contractor and labours are stored in database. Multiple jobs are recommended. Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings every day, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever.

II. PREVIOUS SEARCH

1. Critical Literature review based author studied how to apply the K-Means Clustering method to the job vacancy recommendation system. The weight given shows that the K-Means Clustering method can be applied to the job vacancy recommendation system and can display job recommendations according to the applicant’s personal data. Questionnaire testing is given to applicants, companies, and admins with elements of testing based on user satisfaction, user convenience and system quality, resulting in the conclusion that the system can run well by getting a percentage of 87.6
2. Critical Literature review based Studies have shown that employees who find their job meaningful and satisfying are generally more productive and less likely to leave the job. Human Resource professionals therefore need to ensure that proper screening of candidates is conducted during the recruitment process and that they hire the best fit candidate for a job[2]. The proposed job recommendation system takes as input the requirement of a job and the profile of the applicants and outputs a Job Fit score indicating how fit each applicant is for the particular job. The system ultimately provides the HR professionals with a sorted list of all candidates with those who are more fit and apt for the job recommended first.
3. Critical Literature review based on Job recommender systems are designed to suggest a ranked list of jobs that could be associated with employee’s interest. Most of existing systems use only one approach to make recommendation for all employees, while a specific method normally is good enough for a group of employees. Therefore, this study proposes an adaptive solution to make job recommendation for different groups of user. The proposed methods are based on employee clustering[3]. Firstly, we group employees into different clusters. Then, we select a suitable method for each user cluster based on empirical evaluation. The proposed methods include CB-Plus, CF-jFilter and HyRjFilter have applied for different three clusters. Azlina Johari , his paper presents the development of an onlinebased continuous assessment repository system designed to cater the needs for both lecturers and students.Conventional methods of notifying continuous assessment marks, e.g.,test or assignment marks to students include displaying the students’ marks on the bulletin boards or merely by returning the marked tests or assignments to students.

II. PROPOSED IDEA

The project provides a clear analysis of job recommendation module. Job is predicted to job seeker. Only personal details about the contractor and labours are stored in database. Multiple jobs are recommended. Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings every day, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever.

1. Input Labour data- We give input as data of labour and what are there skills.
2. Input Contractor data - Contractor posting jobs available as suitable location with numbers of labour requirements.
3. Prediction - After entering the data, labours can see the works available
4. Apply algorithms- To recommend jobs we have used SVM classifier that help to predict the suitable jobs.
5. Combine the solution- Both the jobs are match.
6. Map Location - Jobs available on Maps according to location
7. Final Result - Job is recommended on Maps in nearby location of lab

Level-2 DFD goes one step deeper into parts of 1-level DFD.It is used to plan or record the specific/necessary detail about the system functioning.

The figure shows the architecture of our project. All the data from the registration of the user is store in the Firebase. Then the information is transmitted from the Firebase to our app.

a. Registration: If a user need job, he or she must first register and enter the necessary information.

b. Login : User must login with valid email and password

It included functional requirements, non-functional requirements, hardware and software requirements, external requirements, system requirements. This SRS needed to be represented into pictorial form for better understanding. This chapter is about system design. The system design consists of architecture and the system implementation flow. It includes diagrams like system architecture, data flow diagram, use case diagram, activity diagram, class diagram. These diagrams help in understanding the functioning of the system.                                                      

IV. RESULT

The given output shows the information about our App

Conclusion

Recommender systems are a powerful new technology for extracting additional value for a business from its user databases. These systems help users find items they want to buy from a business. Recommender systems benefit users by enabling them to find items they like. Conversely, they help the business by generating more sales. Recommender systems are rapidly becoming a crucial tool in E-commerce on the Web. Recommender systems are being stressed by the huge volume of user data in existing corporate databases, and will be stressed even more by the increasing volume of user data available on the Web. New technologies are needed that can dramatically improve the scalability of recommender systems.

References

[1] Andy Pramono; Aang Kisnu Darmawan,“Implementation K-Means Clustering Method in Job Recommendation System”, Betty Dewi Puspasari; Lany Lukita Damayanti”, 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), IEEE, 2021. [2] Kevin Appadoo; Muhammad Bilaal Soonnoo; Zahra MunglooDilmohamud,“JobFit: Job Recommendation using Machine Learning and Recommendation Engine”, 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, 2021. [3] Gayatri Khanvilkar, Deepali Vora,“Smart Recommendation System Based on Product Reviews Using Random Forest”, 2019 International Conference on Nascent Technologies in Engineering (ICNTE), IEEE, 2019. [4] Amber Nigam, Aakash Roy, Hartaran Singh, Harsimran Waila,“Job Recommendation through Progression of Job Selection”, 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), IEEE, 2019. [5] Vijay Yadav, Ujjwal Gewali, Suman Khatri, Shree Ram Rauniyar, Aman Shakya, “Smart Job Recruitment Automation: Bridging Industry and University”, 2019 Artificial Intelligence for Transforming Business and Society (AITB), IEEE, 2019 [6] S.R. Rimitha; Vedasamhitha Abburu; Annem Kiranmai; K. Chandrasekara, “Ontologies to Model User Profiles in Personalized Job Recommendation”, 2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), IEEE, 2018. [7] Khushbu Jalan; Kiran Gawande,“Context-aware hotel recommendation system based on hybrid approach to mitigate cold-start-problem”, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), IEEE, 2017.

Copyright

Copyright © 2023 Miss. Seema Mane, Abhang Adak, PratapSingh Rathore, Manasi Chaudhar, Santoshi Ubhe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.