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Design of a Recommender System (RS) for Job Searching Using Hybrid System

Received: 24 July 2020     Accepted: 18 December 2020     Published: 22 December 2020
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Abstract

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 paper 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. The hybrid system is composed of Content-Based filtering as well as Knowledge-based Approach which will be has been coded using the Python language. Precise Recommender Systems are very important nowadays.

Published in Internet of Things and Cloud Computing (Volume 8, Issue 3)
DOI 10.11648/j.iotcc.20200803.11
Page(s) 31-40
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Content-Based Filtering, Knowledge-Based Approach, Hybrid-Based Approach Component

References
[1] Jayavardhana Gubb, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami. internet of things (iot): A vision, architectural elements, and future directions. Future generate on computer systems, 29 (7): 1645–1660, 2013.
[2] Aristomenis Lampropoulos and George A Tsihrintzis. Machine Learning Paradigms: Applications in Recommender Systems, volume 92. Springer, 2015.
[3] Gediminas Adomavic us and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. EEE transactions on knowledge and data engineering, 17 (6): 734–749, 2005.
[4] James Bennett, Stan Lanning, etial. The netflix prize. in Proceedings of KDD cup and workshop, volume 2007, page 35. NewYork, NY, USA, 2007.
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[6] James Davidson, Benjamin Liebald, Junning Liu, PalashiNandy, Taylor VaniVleet, Ullas Garg, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, et al. The youtube video recommendation system. in Proceedings of the fourth ACM conference on Recommender systems, pages 293–296. ACM, 2010.
[7] Suresh K Gorakala and M chele Usuelli. Building a recommendation system with R. Packt Publishing Ltd, 2015.
[8] Francesco, Lior Rokach, and Bracha Shapira. ntroduction to recommender systems hand book. in Recommender systems handbook, pages 1–35. Springer, 2011.
[9] Jul e S´egu´ela. Fouille de donn´eesitextuelles et system’s de recommendation appliqués aux off resid employ diffuses surile web. PhD thesis, Paris, CNAM, 2012.
[10] Anil Poriya, Tanvi Bhagat, Neev Patel, and Rekha Sharma. Non-personalized recommender systems and user-based collaborative recommender systems.
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[14] R chard Steinmetz David, Domingo Gregory, Liu A ko, and Tzu-Yu Chen Amy. The restaurant dilemma: Personalized recommendations for groups of people. https://nycdatascience.com/blog/istudent-works/restaurant-recommendations-groups-people/, 2016.
[15] Daniel Tuan. Recommender systems-how they works and their impacts. http://findoutyourfavorite.blogspot.sn/2012/04/content-based-filtering.html, 2015.
[16] John S. Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. in Proceedings of the Fourteenth Conference on Uncertainty and Artificial intelligence, UAI’98, pages 43–52, SanFrancisco, CA, USA, 1998. Morgan Kaufmann Publishers nc.
[17] R. Deckeriand H. J. (Eds.) Lenz, editors. Advances n Data Analysis: Proceedings of the 30th Annual Conference of The Gesellschaft FA˜ 1iriKlassif kation EV, Freie Universitat Berlin, March 8-10. Springer Science and Business Media, 2006.
[18] Paul Resnick, Neophytos acovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. Grouplens: an open architecture for collaborative filtering of net news. in Proceedings of the 1994 ACMiconference on Computer supported cooperative work, pages 175–186. ACM, 1994.
[19] Upendra Shardanand and Pattie Maes. Social nformation filtering: algorithms for automating ’word of mouth’. in Proceedings of the SIGCHI conference on Human factors and computing systems, pages 210–217. ACM Press/Addison-Wesley Publishing Co., 1995.
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Cite This Article
  • APA Style

    Muhammad Bin Abubakr Joolfoo, Radhika Dhurmoo, Rameshwar Ashwin Jugurnauth. (2020). Design of a Recommender System (RS) for Job Searching Using Hybrid System. Internet of Things and Cloud Computing, 8(3), 31-40. https://doi.org/10.11648/j.iotcc.20200803.11

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    ACS Style

    Muhammad Bin Abubakr Joolfoo; Radhika Dhurmoo; Rameshwar Ashwin Jugurnauth. Design of a Recommender System (RS) for Job Searching Using Hybrid System. Internet Things Cloud Comput. 2020, 8(3), 31-40. doi: 10.11648/j.iotcc.20200803.11

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    AMA Style

    Muhammad Bin Abubakr Joolfoo, Radhika Dhurmoo, Rameshwar Ashwin Jugurnauth. Design of a Recommender System (RS) for Job Searching Using Hybrid System. Internet Things Cloud Comput. 2020;8(3):31-40. doi: 10.11648/j.iotcc.20200803.11

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  • @article{10.11648/j.iotcc.20200803.11,
      author = {Muhammad Bin Abubakr Joolfoo and Radhika Dhurmoo and Rameshwar Ashwin Jugurnauth},
      title = {Design of a Recommender System (RS) for Job Searching Using Hybrid System},
      journal = {Internet of Things and Cloud Computing},
      volume = {8},
      number = {3},
      pages = {31-40},
      doi = {10.11648/j.iotcc.20200803.11},
      url = {https://doi.org/10.11648/j.iotcc.20200803.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20200803.11},
      abstract = {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 paper 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. The hybrid system is composed of Content-Based filtering as well as Knowledge-based Approach which will be has been coded using the Python language. Precise Recommender Systems are very important nowadays.},
     year = {2020}
    }
    

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    T1  - Design of a Recommender System (RS) for Job Searching Using Hybrid System
    AU  - Muhammad Bin Abubakr Joolfoo
    AU  - Radhika Dhurmoo
    AU  - Rameshwar Ashwin Jugurnauth
    Y1  - 2020/12/22
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    N1  - https://doi.org/10.11648/j.iotcc.20200803.11
    DO  - 10.11648/j.iotcc.20200803.11
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    UR  - https://doi.org/10.11648/j.iotcc.20200803.11
    AB  - 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 paper 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. The hybrid system is composed of Content-Based filtering as well as Knowledge-based Approach which will be has been coded using the Python language. Precise Recommender Systems are very important nowadays.
    VL  - 8
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Author Information
  • Department of Electrical and Electronic Engineering, University of Mauritius, Reduit, Mauritius

  • Department of Electrical and Electronic Engineering, University of Mauritius, Reduit, Mauritius

  • Department of Electrical and Electronic Engineering, University of Mauritius, Reduit, Mauritius

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