“Catch Geeks” Recommending best freelancer to clients depending on their technology Interest

Abstract

Online outsourcing has plenty of web portal platforms for the freelancers. All the freelancing web portal has to request and hiring functions for the project. Most of the web portals have a similar project function as recommending the projects for the freelancers. The platforms have types of projects which are not relevant to the IT field. These platform clients’ experts find the freelancer for their project by investigating the reviews and rating of the freelancer. The experts don’t recognize the interesting technology and what project has done so far. The quality of the project is reducing by applying a freelancer for the non-interest project. According to this situation, there is no recommending system which recommends the freelancer for project basis on this online outsourcing platforms. In this project, a system developed for recommending the freelancer depends on their past project technology interest. The Catch Geeks system provides a recommending engine only for IT individuals. This system developed by the content based method to find similar projects from the dataset to provide the recommend freelancer details result for the client experts. This system works under the cosine similarity technique provided by the machine learning library to deliver accurate results for the recommending engine. Once the project description added with the technology the recommendation method will work on the similarity calculation to compare the past project description. The client experts can be able to find the freelancer for their relevant projects with interest technology.