Implementation of a Training Courses Recommender System based on k-means algorithm

Providing the right professional training courses for employees is a critical issue for organizations as well as employees. Its necessity stemmed out on the fulfillment of the organization and employees need. Thus, building a recommender system that would help in the decision making process and planning of the training course offered by organizations. This can be performed using various techniques and methodologies, where the most important one is data mining. Data mining is a process of looking for specific patterns and knowledge from large databases and carrying out predictions for outputs. Therefore, this project aims to build a web-based application for predicting appropriate training recommenders for Princess Norah University employees based on their education and professional information. This helps the university in suggesting the most optimal training recommender for employees, which in turn can enhance their performance and develop their career and working levels. Employees’ data was gathered from the Human Resource of the university and then clustered using the WEKA program to find the centroids of clusters to be then used in the developed application. The developed web-based application is used to suggest the most suitable training recommender for each employee. Results demonstrate that the developed web-based application effectively suggests the most appropriate training courses for employees based on the previously taken courses, evaluation of courses and probability for promotion. Furthermore, this web-based application can be used for describing the appropriate training courses for new employees based on their levels. The achieved accuracy of the developed system was 73.33%