Design of a Recommender System for Job Searching Using Hybrid System Python - Based RecommenderSystem Application
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- Englisch ausgewählt
Fr. 55.90
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
21.11.2020
Verlag
LAP LAMBERT Academic PublishingSeitenzahl
60
Maße (L/B/H)
22/15/0.4 cm
Gewicht
107 g
Sprache
Englisch
ISBN
978-620-2-92098-8
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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