Recommendation System for Workers & Customers for Informal Jobs

Abstract

Recommender systems are software applications that provide or suggest items to users. These systems use filtering techniques to provide recommendations. The Purpose of this recommendation system was to provide services to small or part-time workers. It has been observed by the team that recommendation systems that are studied have not focused on small workers like electricians and carpenters but the corporate people. Recent trends in technology have made us dependent on technology too much itself. To cope up with the problems of urbanization and employment trends in this ever-changing world, a well-suited system for corporate workers as well as skilled laborers should coexist. This model is designed to help recruiters to hire employees based solely on work type and rating. A Recruiter can hire a person for a specific task or time frame dependent on what type of employee he/she is expecting. It is designed in such a way that it will help to reduce the gap between both of them leading to a hassle-free experience for an employer as well as an employee. The content-based technique is adopted as it is used to know the content of both user and item. A Manually generated dataset is used by taking the reference of the standard dataset of the formal employees present on the Kaggle website. This system is based on the Vector Space Model and TF-IDF vectorizer. A literature survey of several research papers in the same domain was conducted. The recommendation system has been implemented in the python programming language. The results obtained were quite accurate which helps to recommend jobs to workers and workers to customers in the required work field.