Student career recommendation using content-based filtering method / Adib Hakimi Abdul Rashid - Abdul Rashid, Adib Hakimi (2022)
Finding a suitable career is the most prevalent challenge that students confront following graduation. For students who do not know what they want to be after graduating, career seeking may be a difficult experience. The main aim of this project is to develop a career recommendation system that focuses solely on computer science, specifically for UiTM Tapah's CS230 students. The system's career data was scraped from the Jobstreet website using the web scraping technique. A content-based filtering method is used to make the recommendation, which filters one item to another that is similar to the user's preferences. The Modified Waterfall methodology was used to drive this project, which consists of five (5) phases: planning, analysis, design, development, and testing. Visual Studio Code, Anaconda, Pycharm, and Xampp are among the tools used to create this system. The system is designed with a user-friendly interface and simple procedures for the user to follow in order to make a recommendation. This system was put through its paces with the help of a specialized functionality tester. More career opportunities will be offered to career vacancy websites in the future. The system will be more advanced in terms of screening possible careers for the user to choose from, and it will be linked directly to career page websites to ensure that all open careers are still available for the user to apply for.