Advertisement

A Comprehensive Recommender System for Fresher and Employer

  • Bhavna GuptaEmail author
  • Sarthak Kanodia
  • Nikita Khanna
  • Saksham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 713)

Abstract

Due to overwhelming data on social networking sites about jobs and candidates, it becomes a time-consuming task to generate a match between candidates and employers. Moreover, recruitment of a candidate, who has no work experience called as fresher, poses a two-way problem. Firstly, the candidate due to a lack of experience is not able to decide upon a job among various opportunities which could utilize his/her maximum potential, whereas the employer does not get any past referrals for the candidate to help in the process of recruitment. The proposed study addresses this problem by assisting both; a fresher with a recommended list of job openings which could interest him/her and the employer with a recommendation list of freshers which can be relied upon for the job. The study is assessed and validated with a series of experiments using real data from a social networking site, LinkedIn.

Keywords

Attributes Similarity Ratings Recommender system 

References

  1. 1.
    Al-Otaibi, S.T., Ykhlef, M.: Job recommendation systems for enhancing e-recruitment processGoogle Scholar
  2. 2.
    Hong, W., Zheng, S., Wang, H.: A job recommender system based on user clustering. J. Comput. (2013)Google Scholar
  3. 3.
    Al-Otaibi, S.T., Ykhlef, M.: A survey of job recommender system. Int. J. Phys. Sci. (2012)Google Scholar
  4. 4.
    Koh, M.F., Chew, Y.C.: Intelligent job matching with selflearning recommendation engine. Els. J. (2015)Google Scholar
  5. 5.
    Diaby, M., Viennet, E., Launay, T.: Exploration of methodologies to improve job recommender systems on social networks, Springer,Wien (2014)Google Scholar
  6. 6.
    Liu, R., Ouyang, Y., Rong, W., Song, X., Xie, W., Xiong, Z.: Employer oriented recruitment recommender service for university students. Spr. J. (2016)Google Scholar
  7. 7.
    Musale, D.V., Nagpure, M.K., Patil, K.S., Sayyed, R.F.: Job recommendation system using profile matching and web-crawling. Int. J. Adv. Sci. Res. Eng. Trends (2016)Google Scholar
  8. 8.
    Gillet, D., Lu, Y., El Helou, S.: Analyzing user patterns to derive design guidelines for job seeking and recruiting website. École Polytechnique Fédérale de Lausanne (2012)Google Scholar
  9. 9.
    Liu, R., Ouyang, Y., Rong, W., Song, X., Tang, C., Xiong, Z.: Rating prediction based job recommendation service for college students. Spr. J. (2016)Google Scholar
  10. 10.
    Shi, S.: Real-time job recommendation engine based on college graduates’ personal. J. Res. Sci. Technol. (2016)Google Scholar
  11. 11.
    Kille, B., Abel, F.: Engaging the crowd for better job recommendations, CrowdRec (2015)Google Scholar
  12. 12.
    Pizzato, L., Rej, T., Chung, T., Yacef, K., Koprinska, I., Kay, J.: Reciprocal Recommenders, University of Sydney (2006)Google Scholar
  13. 13.
    Gillet, D., Lu, Y., El Helou, S.: A recommender system for job seeking and recruiting website. École Polytechnique Fédérale de Lausanne (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bhavna Gupta
    • 1
    Email author
  • Sarthak Kanodia
    • 1
  • Nikita Khanna
    • 1
  • Saksham
    • 1
  1. 1.Department of Computer ScienceKeshav Mahavidyalaya, University of DelhiNew DelhiIndia

Personalised recommendations