A Book Recommender System Using Collaborative Filtering Method

Abstract

Recommender systems are used to generate meaningful recommendations to users based on their preferences, which will be determined following several approaches. This work targets Arab readers by providing accurate and reliable results that match their needs and desirability. Eventually, it will enhance the reading experience for any Arab readers. The main approach is to filter the recommendations, and this can be achieved either by Content-Based filtering or by Collaborative Filtering. The collaborative filtering techniques presented in this paper compute the similarity matrix between items and users' ratings, and then evaluate the recommendations for users. The techniques cover User-Based and Item-Based Collaborative Filtering, as well as Matrix Factorization through an SVD algorithm. A comparison between these techniques is presented in terms of the fitting and testing time, and accuracy. The KNN-based algorithms showed better performance than the matrix factorization method with respect to fitting and testing time. However, the matrix factorization (SVD) algorithm had the best results in terms of accuracy.