Citation

Fei, Yap Peak and Ting, Choo-Yee and Abdul Rashid, Hairul Azhar (2025) Optimising iCadet Assignment through User Profiling. JOIV : International Journal on Informatics Visualization, 9 (1). p. 231. ISSN 2549-9610

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Abstract

Industry Cadetship programme is a programme that assigns penultimate year students to companies matching their profiles, bridging academic learning and industry skills. Manual data analysis for assignments is time-intensive, prompting this study’s objectives: (i) propose an algorithm to optimize student-company assignment by using the student and company profiles, (ii) propose a method for the assignment of lecturers to company, and (iii) use similarity measure techniques to recommend companies with similar characteristics. Data was collected from a university's student, company, and lecturer datasets. To assign students to companies, the Haversine, OpenStreetMap, and NetworkX were used to calculate the shortest geographical distance between the students and the companies; evaluated based on mean, variance, standard deviation, and utilization rate. For the lecturer assignment, cosine similarity was applied to measure the similarity between domain descriptions and company or lecturer information after performing Voyage AI embeddings. Lecturers are assigned to companies based on the highest domain similarity scores. The performance was evaluated using accuracy, precision, recall, and F1- score. Findings showed embedding techniques significantly enhanced the matching process, with accuracy improved from 0.464 to 0.6071, precision increased from 0.417 to 0.5058, recall saw an equal rise from 0.464 to 0.6071, and the F1-score advanced from 0.417 to 0.5264. Longer descriptive inputs further improved performance, with accuracy rising from 0.6154 to 0.7692, precision from 0.5744 to 0.7751, recall remaining steady at 0.7692, and F1-score increasing from 0.5807 to 0.7484. This work can be extended to explore job portal dataset by aligning profiles with geography and specialization.

Item Type:	Article
Uncontrolled Keywords:	iCadet; user profile; company profile; similarity measure; matching algorithm
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	10 Apr 2025 02:31
Last Modified:	10 Apr 2025 02:31
URII:	http://shdl.mmu.edu.my/id/eprint/13661

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