Design of a Recommender System for Job Searching Using Hybrid System: Python ? Based RecommenderSystem Application

Joolfoo, Muhammad Bin Abubakr; Joolfoo, Muhammad Khalid Bin Abubakr

ISBN 10: 620292098X / ISBN 13: 9786202920988

Editore: LAP LAMBERT Academic Publishing, 2020

Riguardo questo articolo

PRINT ON DEMAND.

Codice articolo 18397293786

Dati bibliografici

• Titolo: Design of a Recommender System for Job Searching Using Hybrid System: Python ? Based RecommenderSystem Application
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 620292098X
• ISBN 13: 9786202920988
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

By and large, searching for work while examining a rundown of enlisting positions on enrollment locales, which truly cost a lot of time and cash is an irritating thing to do Although most of the time those jobs are not always suitable with users, or users are not satisfy. By doing this, recruiters waste their time by making sure that they are qualify or not. This thesis seeks to address a very important issue on the recruitment process which is about matching jobs seekers with jobs offers. These days, the coordinating procedure between the candidate and the activity offers is one of the serious issue’s organizations need to deal with. Short listing candidates and screening resumes are long time-consuming tasks for the company, especially when 80 percent to 90 percent of the resumes received for a role are unquailed. We have designed and proposed a hybrid personalized recommender system used for job seeking and online recruiting websites adapted to the cold start problem using a collaborating predictive algorithm.