Recommendation System for Academic Queries Using Machine Learning

Abstract

One kind of recommendation system that uses human expertise to provide individualized recommendations is the academic query recommendation system. Because the recommendations made by these systems are based on the knowledge and experience of actual people, they are frequently more reliable and accurate than those made by fully automated systems. The following elements are required to create a recommendation system for academic queries: feature extraction and selection, user interface design, data collecting and preprocessing, and recommendation algorithm development. In order to promote student participation and interaction, the system can also make use of social networks and collaboration tools. An expert recommendation system can help college students become more motivated and engaged, perform better academically, and have better career outcomes. The system can benefit universities as well as. There are numerous courses accessible on e-learning platforms; users must choose the one that best suits their needs. Recommendation systems are crucial in enabling users to make better automated course selections. It suggests that consumers choose the option of their choice according to their preferences. This technology can determine the users' preferences and past usage to determine what they enjoy most. The suggested work might assist students in choosing online courses based on their preferences. Keywords: Recommendation System, Human Expertise, Machine learning