Paper Key : IRJ************948
Author: Ritesh Santosh More,Madhura Vikas Khande,Prof.jagtap V.g.,Akash Vikram Mulik,Mahesh Bramhdev Shinde
Date Published: 10 Nov 2024
Abstract
In today's competitive environment, skill development and access to quality education are crucial for career advancement. Numerous online course recommendation systems have been created in the field of personalized course-learning services to meet the various needs of students. However, the abundance of options of offline classes can also create confusion and make it difficult for students to decide on classes to be taken, especially when considering their career aspirations. However, despite these advancements, there still exist three unsolved challenges: 1) how to effectively recommend the offline classes if students want to search offline classes. 2) how to identify the high-correlated classes in the class corpora. 3) Offline classes recommendation on various platforms may contain fake reviews & ratings. To address this problem, the proposed system introduces an integrated offline classes and jobs recommendation system that utilizes machine learning techniques like knowledge graph, Natural Language Processing, decision tree to suggest relevant courses and employment prospects according to the student's interests, abilities, and professional objectives. An offline course recommendation system uses user skills and class information content, ratings & reviews to suggest personalized classes. Our system uses MERN Stack technology & ML algorithms like Natural Language Processing for review analysis & Knowledge graph to make personalized recommendations. System can authenticate the ratings & reviews given by students to respective classes. The job and course recommendations are the two main components of the system. The course recommendation feature makes recommendations for appropriate classes to the student based on their interests and profile. The job recommendation component helps students find appropriate jobs based on their qualifications, experience, and desired careers. The suggested classes and job suggestion system may help students make well-informed choices regarding their educational and professional pathways. Furthermore, the system can be extended to recommend offline classes with data given by users like location i.e. if users want to search classes within specific areas.
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