Zero-Shot Recommendation AI Models for Efficient Job–Candidate Matching in Recruitment Process
In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like...
Main Authors: | Jarosław Kurek, Tomasz Latkowski, Michał Bukowski, Bartosz Świderski, Mateusz Łępicki, Grzegorz Baranik, Bogusz Nowak, Robert Zakowicz, Łukasz Dobrakowski |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-03-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/14/6/2601 |
Similar Items
-
Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives
by: Deepak Kumar, et al.
Published: (2023-10-01) -
Automatic Taxonomy Classification by Pretrained Language Model
by: Ayato Kuwana, et al.
Published: (2021-10-01) -
ESG2PreEM: Automated ESG grade assessment framework using pre-trained ensemble models
by: Haein Lee, et al.
Published: (2024-02-01) -
Research Progress on Vision–Language Multimodal Pretraining Model Technology
by: Huansha Wang, et al.
Published: (2022-10-01) -
Personalized Re-ranking for Recommendation with Mask Pretraining
by: Peng Han, et al.
Published: (2023-09-01)