A preprocessing matrix factorization on collaborative filtering based library book recommendation system

Abstract

Nowadays, recommendation systems are widely used to recommend items to the users that are specific to their individual preferences and most appropriate. For this reason, many academic libraries try to establish an effectiveness and efficiency book recommendation system which could enhance students' performance. This research presents the process of book recommendation by using the collaborative filtering (CF), one of the most popular techniques widely used in recommendation systems, for university students. Since data sparseness is the one key issue limiting the success of collaborative filtering, we also adopt bias matrix factorization technique to handle this problem. Book recommendation of each student has been generated by using existing borrowing records with a time stamp. In our experiments, different techniques of similarity calculation are compared. The performance evaluations are conducted using both accuracy measure and student satisfaction evaluation with the book recommended by the system.