The Development a Job Position Recommendation System Using Collaborative Filtering Technique
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Abstract
Unemployed individuals and recent graduates often lack the experience or skills necessary to effectively search and filter job positions on job search websites, hindering their ability to access job listings and requirements. To address this issue, a research process was conducted involving extracting job information from the top job search website in Thailand. The data was then prepared, transformed, and subjected to Matrix Factorization. The data was randomly split into 80% for training and 20% for testing, out of a total of 30,000 records. This data was used to test the performance of a recommendation system utilizing the Collaborative Filtering technique. The Learning Curve, which measures the efficiency of model learning through Training Loss and Validation Loss, showed a continuous decrease in value until it reached its lowest validation loss of 0.0442, indicating a well-fitted model. This research suggests that the model can be effectively used for predicting and creating a prototype of a job recommendation system that connects job opportunities via API to efficiently meet the needs of job seekers and recent graduates.
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