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Candidate - job recommendation system: Building a prototype of a machine learning – based recommendation system for an online recruitment company
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Linneuniversitetet.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Recommendation systems are gaining more popularity because of the complexity of problems that they provide a solution to. There are many applications of recommendation systems everywhere around us. Implementation of these systems differs and there are two approaches that are most distinguished. First approach is a system without Machine Learning, while the other one includes Machine Learning. The second approach, used in this project, is based on Machine Learning collaborative filtering techniques. These techniques include numerous algorithms and data processing methods. This document describes a process that focuses on building a job recommendation system for a recruitment industry, starting from data acquisition to the final result. Data used in the project is collected from the Pitchler AB company, which provides an online recruitment platform. Result of this project is a machine learning based recommendation system used as an engine for the Pitchler AB IT recruitment platform.

Place, publisher, year, edition, pages
2019. , p. 48
Keywords [en]
machine learning, recommendation systems, collaborative filtering, mode-based, matrix factorization, data analysis, python, supervised learning, recruitment platform, singular value decomposition, non-negative matrix factorization
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:lnu:diva-89778OAI: oai:DiVA.org:lnu-89778DiVA, id: diva2:1365184
External cooperation
Pitchler AB
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 60 credits
Presentation
2019-09-19, K1050V, 17:54 (English)
Supervisors
Examiners
Available from: 2019-10-24 Created: 2019-10-23 Last updated: 2019-10-24Bibliographically approved

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Candidate - job recommendation system(900 kB)22 downloads
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18b58e0254deb13bed73582b28d9bad7f72a7a8ec1b990305885ca5d04ccc3686ea9385c8bd0b351956ebabf700f433a569bf8f9ca58f6b690ddd4e469a1879c
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf