A HYBRID RECOMMENDER FRAMEWORK FOR SELECTING A COURSE REFERENCE BOOKS
Recommender systems are receiving great attention these days, as various researchers and major companies are conducting continuous research in this field. Companies like Google and Amazon have provided different effective models for video recommendation systems, but the educational field is poorly studied as other researchers explained. Different researchers proposed various approaches showing the challenges related to recommender systems and have proposed various effective recommender systems. This paper aims to propose a hybrid recommender framework that can recommend educational courses' books to study with high accuracy and efficiency. The proposed framework is a hybrid unified approach that helps those who desire to be taught to get suitable books related to a specific course description when a course description is used as an input. This work proposes three different recommendation algorithms for building a hybrid recommendation system. One of the algorithms uses an association rule algorithm to automatically and intelligently guide the end-user to find the most relevant materials. © 2022 Little Lion Scientific.