• Ekle Francis Adoba
• Ebem Deborah Uzoamaka
• Francis Bem Vaatyough
• Modesta Ezema

Nigeria is the most populous black nation in the world, located in the western region of Africa, with over 100 universities and a very large population of students and lecturers cut across several campuses, colleges, faculties, departments, and specialties. Recommender systems are software tools mostly used on e-commerce websites that help management find and recommend appropriate products and services to customers. This research develops a hybrid-based recommender system for recommending books to students, lecturers, and researchers that patronize university bookshops based on their preferences. The problems encountered in manual recommendations and electronically built recommender systems developed using only one recommendation technique have seriously affected the quality of recommendations made by such recommender systems. This has often resulted in the wrong choice of book recommendations among recommender systems, leading to a lack of motivation and encouragement in customers’ reading habits and culture. This research therefore implements a switching and cascade-based hybrid recommender system by combining content-based, demographic, and collaborative filtering recommendation techniques into a single system. A user-based approach of collaborative filtering recommender technique was used for developing this application. This was achieved using the K-Nearest Neighbors (kNN) classification machine learning algorithm for grouping similar users together based on explicit data gotten from their personal ratings for books they have purchased and read. The demographic profile of the users and the features of books were used for both demographic and content-based recommendation techniques, respectively. The system was implemented using a Python web-based programming language called Django and a MySQL database.

Recommender system, Content-based filtering, Demographic filtering, Collaborative filtering, Book, Switching, Cascade and Nigerian university bookshops.

"A Switching and Cascade-Based Hybrid Recommender System for Nigerian University Bookshops", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.d458-d470, May-2022, Available :http://www.jetir.org/papers/JETIR2205477.pdf

"A Switching and Cascade-Based Hybrid Recommender System for Nigerian University Bookshops", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppd458-d470, May-2022, Available at : http://www.jetir.org/papers/JETIR2205477.pdf