Abstract
The rise of digital communication and the spread of the internet has made an
enormous impact in every industry. One such domain is the Hiring process, where
a job seeker applies to a job by creating a profile on a job portal by providing all
his/her work preferences. These work preferences of each user can be collected
from each user and provide job recommendations based on their preference. There
had been work done in this field, where researchers have implemented Recsys using
the Hybrid filtering method as user data had previous interaction with item (Rafter
et al., 2000).In this dissertation, we have approached the problem with the three-tier
approach design. Data acquired for our study has no previous interaction between
the user data and Job listing data. With such a dataset, we have addressed the issue
of cold start from both User and Job perspective. Also, recommend the top-n job
to the user by analyzing and measuring similarity between the user preference and
explicit features of job listing using Content-based filtering, which is devised in
support of natural language processing and cosine similarity. The Recommender
System is then evaluated using precision, recall, and F1 score(Barrón-Cedeno et al.,
2009). The top-n recommendation made to the user is presented in the third tier of
the design, a web app deployed in the local server. The presentation layer web-app
is developed using Plotly’s dash web framework.
