Authors :
Uday Jain; Daksh Jain; Aditya Raj Varshney
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/2c53y5kf
Scribd :
https://tinyurl.com/56tscbns
DOI :
https://doi.org/10.5281/zenodo.10200263
Abstract :
Online job portals have rapidly expanded,
making it simpler for job searchers to find employment.
But it can take much work for job searchers to find the
ideal position that matches their skills and preferences
due to the abundance of job postings. To solve this issue,
the author present a system for recommending relevant
job listings to students using machine learning and
natural language processing techniques. There has never
been any prior interaction between user data and job
listing data in the dataset collected for our research. The
system employs a hybrid strategy to generate precise
suggestions, combining collaborative filtering and
content-based filtering. To provide the most pertinent
job suggestions, the system examines the student's
resume, specifications, and posting. Additionally, the
system suggests the top jobs to the user by analyzing and
gauging the similarity between the user choice and
explicit job listing features. The Recommender System is
then evaluated using precision, recall, and F1 score.
Keywords :
NLP, Cosine Similarity, Word2Vec, Content- Based Filtering.
Online job portals have rapidly expanded,
making it simpler for job searchers to find employment.
But it can take much work for job searchers to find the
ideal position that matches their skills and preferences
due to the abundance of job postings. To solve this issue,
the author present a system for recommending relevant
job listings to students using machine learning and
natural language processing techniques. There has never
been any prior interaction between user data and job
listing data in the dataset collected for our research. The
system employs a hybrid strategy to generate precise
suggestions, combining collaborative filtering and
content-based filtering. To provide the most pertinent
job suggestions, the system examines the student's
resume, specifications, and posting. Additionally, the
system suggests the top jobs to the user by analyzing and
gauging the similarity between the user choice and
explicit job listing features. The Recommender System is
then evaluated using precision, recall, and F1 score.
Keywords :
NLP, Cosine Similarity, Word2Vec, Content- Based Filtering.
