ABSTRACT
Due to the events caused by the COVID-19 pandemic, the education industry is no longer limited to offline, and online classroom education is widely used. The rapid development of online education provides users with more abundant educational course resources and flexible learning methods. Various online education platforms are also constantly improving their service models to give users a better learning experience. However, at present, there are few personalized information recommendation services in student course selection. Students receive the same course selection information and cannot be "tailored" according to their specific preferences. This paper focuses on the integration of collaborative filtering technology into a college course selection system to construct a rating matrix based on students' ratings of the courses they take through correlation between courses and correlation between students. Based on the collaborative filtering algorithm, a predictive rating matrix is generated to produce a recommendation list to achieve intelligent recommendation of suitable courses for students. The experimental results show that, based on the traditional collaborative filtering recommendation technique, the improved collaborative filtering algorithm based on both item and user weighting is used to achieve course recommendation with higher recommendation accuracy. The application of the improved collaborative filtering technique in the course selection recommendation system of colleges and universities is very good at recommending courses for students intelligently, and the recommended courses for students have good rationality and accuracy, and achieve more intelligent course selection for students, which has great practicality and practical significance.
ABSTRACT
Before the modern era of personal computing, it was impossible to collect and analyze data in a wide variety of methods found on big data technology. The analytical knowledge of big data room for people to identify a new cures and better understanding a number of diseases and health care. Hence, there are a number of issues that need to be further addressed. The major focus of this study is to deliver a review of studies that utilized big data issues during pandemic COVID-19 in 2020. The study is implemented by ten phases, includes defining research questions, scope review, conduct study, extracting all papers, paper screening, relevant papers, search more specific keywords, classification scheme, data extraction and systematic map. A detailed review studies were selected from January 2020 until December 2020 based on sources from Web of Science (WoS). This study highlights three key words: big data, business intelligence and business analytics. The findings of the study show that, the field of computer science is the most frequent publication in 2020 with 14 journal publications related to the field of computer science. Nevertheless, Sustainability journal showed the highest number of publications in 2020. Followed by the journals of Future Generation Computer Science and Basic and Clinical Pharmacology & Toxicology, Applied Science-Basel, and the International Journal of Information Management. © 2021 IEEE.