A recommender system for selecting potential industrial training organizations

The difficulty in securing students industrial work experience scheme (SIWES) placements has negatively affected the final grades of some undergraduates. A recommender system is hereby proposed to solve this problem. This system will use past SIWES data to recommend potential organizations to future SIWES students. Research data collection was through an online questionnaire, with 200 respondents. The data was divided into a training data set (70%) and test data set (30%). Collaborative filtering recommendation approach was employed using the C4.5 algorithm to classify the data and generate a decision tree model from the training data set. The model generated was used to predict the class label of the test data set. Results from the data analysis carried out revealed that Kappa statistics was 0.7839, mean absolute error = 0.0058, and root mean squared error = 0.0586. In addition, true positive rate = 0.788, recall = 0.788, precision = 0.749, F‐measure = 0.755, MCC = 0.760, ROC = 0.985, and PRC = 0.813. The high values obtained indicates that the model was predicting with a high level of accuracy, ie, 78.84% correctly classified instances. The developed model was used as a knowledge base to develop a very beneficial front‐end web application, tagged “RecommendIT” where students can enter their preferences and view company recommendations.