Providing Personalized Services to Users in a Recommender System

Olukunle Oduwobi and Bolanle Adefowoke Ojokoh
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Olukunle Oduwobi: Federal University of Technology, Akure, Nigeria
Bolanle Adefowoke Ojokoh: Department of Computer Science, Federal University of Technology, Akure, Nigeria

International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2015, vol. 10, issue 2, 26-48

Abstract: Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

Date: 2015
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International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) is currently edited by Mahesh S. Raisinghani

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