This paper is published in Volume-6, Issue-4, 2020
Area
Computer Science And Engineering
Author
Swathi V., Suraksha S. Tasgaonkar, Shubhashri T. V., Prarthana T. V.
Org/Univ
BNM Institute of Technology, Bengaluru, Karnataka, India
Pub. Date
12 August, 2020
Paper ID
V6I4-1369
Publisher
Keywords
Recommender Systems, Machine Learning, Candidate Recommender, K-Nn, Matrix Factorization, Collaborative Filtering

Citationsacebook

IEEE
Swathi V., Suraksha S. Tasgaonkar, Shubhashri T. V., Prarthana T. V.. Candidate talent assessment through recommender systems using machine learning techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Swathi V., Suraksha S. Tasgaonkar, Shubhashri T. V., Prarthana T. V. (2020). Candidate talent assessment through recommender systems using machine learning techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 6(4) www.IJARIIT.com.

MLA
Swathi V., Suraksha S. Tasgaonkar, Shubhashri T. V., Prarthana T. V.. "Candidate talent assessment through recommender systems using machine learning techniques." International Journal of Advance Research, Ideas and Innovations in Technology 6.4 (2020). www.IJARIIT.com.

Abstract

The world is moving towards complete automation where most of the systems are being automated, one of the examples of this trend is the automation of IT industry. The candidate recommender system helps in the selection of candidates for a company. The candidate recommender system looks into the various profile of the candidates chooses a Candidate whose profiles best matches that of the company and the job.