文章基本信息

标题：Taxonomy of Recommender Systems for Educational Data Mining (EDM) Techniques: A Systematic Review
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作者：Mosima A. Masethe ; Sunday O. Ojo ; Solomon A Odunaike 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2019
卷号：2241
页码：243-248
出版社：Newswood and International Association of Engineers
摘要：The educational management decision-makers (EMDMs) have no clear understanding of the available EDM techniques and variables to consider in selecting EDM techniques appropriate for their decision making needs. This research proposes taxonomy of EDM techniques through utilisation of recommender systems (RSs) to address EDM technique selection challenges. The RS approach addresses the need for a decision support system guide process of selecting appropriate EDM technique. Furthermore, the research presents systematic review of different approaches in recommender system such as content-based, collaborative filtering, hybrid, and knowledge-based recommender system. The research study looks at current existing challenges of different recommender system including proposed technique to solve the problems. Knowledge-based recommender system seems to solve problems encountered by content-based recommender system and collaborative filtering recommender system. The research further discus how knowledge-based RS notable employs case-based reasoning and ontology-based engineering to overcome challenges such as cold-start, scalability, sparseness, grey sheep, contend limitations, overspecialization, and inflexible information.
关键词：Educational Data Mining (EDM);; Recommender System (RS); Case-Based (CBR) RS;; Knowledge;Based (KB) RS