A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

Algorithms

Meng-Hui Chen¹  Cheng-Hui Lin²  Pei-Chann Chang³ 

[1] Digital Convergence and Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan;;Innovation Center for Big Data &Software School, Nanchang University, Nanchang 330029, China;

关键词: course recommendation system;    collaborative filtering;    artificial immune system;    confusion matrix;    cluster analysis;   

DOI  :  10.3390/a9030047

来源: DOAJ

【 摘 要 】

This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.

【 授权许可】

Unknown