A Goal-Based Hybrid Filtering for Low-rated Users Recommendation Issue Using Neighborhood Personalized Profile Similarities in E-Learning Recommendation Systems

Part of the Studies in Computational Intelligence book series (SCI, volume 551)

Abstract

The e-learning recommender systems are based on the users past history, ratings, likes or dislikes. The low-rated (less rated history profile) users may cause of zero or non-relevant recommendations issue in these days, which lose the users interest. This research proposed the goal-based hybrid filtering approach that used to perform the personalized similarities between users personalized profile preferences collaboratively. The aim of this research study is to improve the low-rated user’s recommendations by tackling the collaborative filtering and k-neighborhood personalized profile preferences similarities in e-Learning recommendation scenarios. The experiments has been tackled with famous ‘Movielens’ dataset while the experimental results has been performed with the help of (average mean precision Pr: 79.90%) and (average mean recall Re: 83.50%) respectively. A conducted result demonstrates the effectiveness of proposed goal-based hybrid filtering in the improvement of low-rated users profile recommendations in e-learning recommendation systems.

Keywords

Goal-based hybrid filtering recommender systems e-learning collaborative filtering k-nearest neighbors 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Software Engineering Department, Faculty of ComputingUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

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