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

Editorial: LAP LAMBERT Academic Publishing, 2020

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa blanda

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N� de ref. del art�culo 26397293776

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Detalles bibliogr�ficos

T�tulo

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

Editor

LAP LAMBERT Academic Publishing

Fecha de publicaci�n

2020

Idioma

Ingl�s

ISBN 10

620292098X

ISBN 13

9786202920988

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

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Sinopsis

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.

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I mainly carry imported books from South East Asia / South Asia for readers of all Age Groups.

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