Semantic P2P-Based Learning Resources Personalized Recommendation System Design

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

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E-commerce Personalized Recommendation System Design Based on Multi-agent

Advances in Intelligent and Soft Computing - Advances in Computer Science and Information Engineering ◽

10.1007/978-3-642-30126-1_26 ◽

2012 ◽

pp. 159-164 ◽

Cited By ~ 1

Author(s):

Ya Luo

Keyword(s):

System Design ◽

Recommendation System ◽

Personalized Recommendation ◽

Multi Agent

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The Study of User Model of Personalized Recommendation System Based on Linked Course Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.519-520.1609 ◽

2014 ◽

Vol 519-520 ◽

pp. 1609-1612

Author(s):

Ji Yan Wu ◽

Chan Le Wu

Keyword(s):

Linked Data ◽

Recommendation System ◽

User Model ◽

Personalized Recommendation ◽

Learning Resources ◽

Learning Path ◽

E Learning ◽

Linked Courses ◽

Efficient Learning ◽

Massive Information

The massive information on Internet makes users obtain information become in efficient.Similarly,the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable,etc .Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental ,the application shows that the model can indicate the user's learning requirements,and rational and efficient learning path is recommended.

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Design of personalized recommendation system for online learning resources based on improved collaborative filtering algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/202021401051 ◽

2020 ◽

Vol 214 ◽

pp. 01051

Author(s):

Baiqiang Gan ◽

Chi Zhang

Keyword(s):

Online Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Personalized Recommendation ◽

Online Course ◽

Learning Resources ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm ◽

Open Courses ◽

Online Learning Resources

In recent years, under the guidance of the educational concept of equality and sharing, universities at home and abroad have increased the development and application of online course learning resources. In China, online open courses are open to all learners on the platform of major portals. Due to the increasing number of online courses, it is increasingly difficult for learners to find the content they are interested in on the website. In addition, the traditional collaborative filtering has the problems of sparse data, cold start, and low accuracy of recommendation results, etc. Therefore, the personalized recommendation system studied in this paper adds the collaborative filtering recommendation technology of user and project attributes. The recommendation system can actively discover the interest of learners according to their behavior characteristics, and provide them with online learning resources of interest, and improve the accuracy of the recommendation results by improving the collaborative filtering algorithm. In this paper, personalized recommendation technology is applied to online course website, aiming at providing personalized, automated and intelligent recommendation system for online learners.

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Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2022.16.16 ◽

2022 ◽

Vol 16 ◽

pp. 122-131

Author(s):

Mingxia Zhong ◽

Rongtao Ding

Keyword(s):

Online Learning ◽

Collaborative Filtering ◽

Recommendation System ◽

Personalized Recommendation ◽

Learning Resources ◽

Learning Behaviors ◽

Filtering Algorithm ◽

Learning Platform ◽

Collaborative Filtering Algorithm ◽

Learning Platforms

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

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