Improving Effectiveness of Industrial Placement Experience Using a Recommender System
Loading...
Date
2023
Authors
Ndolo, Daniel Mulinge
Journal Title
Journal ISSN
Volume Title
Publisher
Kenyatta University
Abstract
Different classifier systems have been developed as a result of technological advancement to
replace traditional job-search techniques. Finding locations for their industrial attachments is
difficult for students in tertiary institutions. By making this training option, they are restricted to
open elements like geographic coverage and a limited understanding of industry players. This
study used mixed research methodology to conduct its investigation which included survey
research and software development methodologies. A multi-criteria classifier called PlacementKe was developed to create user profiles that are used to forecast and provide recommendations
while matching a student with the right company for their industrial attachment training. The
classifier was based on the hybrid collaborative filtering algorithm. The recommendations
generated were anchored on the user profiled interest and weighted ratings. Using a preexisting
data set produced by Kaggle, a base model was created using a pre-trained base model. The base
model’s accuracy level was 92%. The base model was used to create parameters that were applied
in the development of a custom placement recommender system. The model was implemented to
a prototype application, which was evaluated using actual users and data. The user comments were
recorded on a survey form and examined. According to the analysis, 77.78% of users were happy
with the system’s overall performance. The inquiry used in the literature evaluation revealed a
need for automation in industrial attachment placements to increase their efficiency and speed.
The qualitative and quantitative objectives that guided the system design were both met by the
research design. The system designed achieved a high user satisfaction rate. This study suggests
that the system be developed and implemented for use in higher education institutions. Future
work extensions are also discussed.
Description
his Research Project is Submitted for the Partial Fulfillment of the Requirements for the Award of the Degree of Masters of Science in Computer Science in the School of Engineering and Technology of Kenyatta University, February 2023.
Keywords
Recommender engines, filtering technique, mixed hybridization, job recommender systems, industrial placements, machine learning, algorithms, data structures