Authors :
Daniel Mulinge Ndolo
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3K8ALYn
DOI :
https://doi.org/10.5281/zenodo.7797418
Abstract :
This paper comparatively reviews the
different recommendation models amidst the different
classifier filtering techniques. Recommender systems
have been adopted in other areas of life to provide
custom-made solutions to netizen. The accuracy or
weakness of a recommender system is based on the
recommendation approach and filtering techniques
adopted. Recommenders’ systems are developed to suit
the needs and personal interests of the users. This paper
review looks into previous and current recommender
systems with a view of the different characteristics,
advantages and disadvantages. This paper also
undertakes a comparative review of hybrid
recommendation techniques as regards to improving job
recommendation systems.
Keywords :
Recommender System, Filtering Techniques, Hybrid Models, E-Recruitment, Collaborative Filtering, Content-Based, Knowledge-Based Recommendation Approach.
This paper comparatively reviews the
different recommendation models amidst the different
classifier filtering techniques. Recommender systems
have been adopted in other areas of life to provide
custom-made solutions to netizen. The accuracy or
weakness of a recommender system is based on the
recommendation approach and filtering techniques
adopted. Recommenders’ systems are developed to suit
the needs and personal interests of the users. This paper
review looks into previous and current recommender
systems with a view of the different characteristics,
advantages and disadvantages. This paper also
undertakes a comparative review of hybrid
recommendation techniques as regards to improving job
recommendation systems.
Keywords :
Recommender System, Filtering Techniques, Hybrid Models, E-Recruitment, Collaborative Filtering, Content-Based, Knowledge-Based Recommendation Approach.
