Improving Effectiveness of Industrial Placement Experience Using a Recommender System

Thumbnail Image
Ndolo, Daniel Mulinge
Journal Title
Journal ISSN
Volume Title
Kenyatta University
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.
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.
Recommender engines, filtering technique, mixed hybridization, job recommender systems, industrial placements, machine learning, algorithms, data structures