Job and Course Recommendation System using Collaborative Filtering and Naive Bayes algorithms

Abstract

In recent years, the education system has undergone a significant transformation due to technological advancements. With the emergence of online learning platforms, students can now access an enormous amount of course materials, resources, and lectures online. However, the abundance of options can also create confusion and make it difficult for students to decide on courses to be taken, especially when considering their career aspirations. To address this problem, the proposed system introduces an integrated course and job recommendation system that utilizes machine learning techniques like Naïve Bayes and Collaborative filtering algorithms to suggest relevant courses and job opportunities based on the student’s interests, skills, and career goals.Our system uses a hybrid approach that combines naïve bayes and collaborative filtering techniques to make personalized recommendations. The system consists of two main components: course recommendation and job recommendation. The course recommendation component suggests relevant courses to the student based on their profile and interests. The job recommendation component suggests suitable job opportunities to the student based on their skills, experience, and career aspirations. The proposed course and job recommendation system has the potential to assist students in making informed decisions about their education and career paths. Furthermore, the system can be extended to incorporate additional data sources such as student feedback, industry trends, and job market demands to further enhance the accuracy and relevance of recommendations.