期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2017
卷号：12
期号：6
页码：52-76
语种：English
出版社：Kassel University Press
其他摘要：Nowadays, Information and communications technology (ICT) becomes a very important thing in human life in different fields. They are used in many fields as information systems (software, middleware) using various telecommunication media to give users the ability to manipulate digital data. In addition, with new technology development, a new concept appeared in the late 90s and early millennium, which is distance learning through e-Learning platform. Recommendation systems become increasingly used in information systems and especially in e-learning platform. These systems are used to propose and recommend content of these platforms to users according to needs of the latter in order to allow them to have the maximum information for learning.
In this paper, we present an intelligent hybrid recommendation system based on data mining. This system has four parts, the first for data collection and for center of interest construction by two modes: explicit data collection, which based on users and what they filled in their profiles, and implicit and automatic data collection by proposing a survey to users in order to gather information about their interest. A second part for processing information already collected in the previous part and for creating the learning model, classifying users who posted the content and classifying content also in order to send the results to the recommendation module. The third part is for making the similarity between learners and content and doing the recommendation for learners and the final part is for creating a log file of recommendation by learner, which will be used in the upcoming recommendation. According to results already done, we noticed that our proposition is satisfactory and the system is well optimized in terms of accuracy, response and processing time compared to the standard recommendation.
关键词：e-learning;recommendation systems;filtering based content;collaborative filtering;hybrid filtering;utility matrix;data mining;decision trees
