Abstract
With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner’s basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner’s score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.
Highlights
In recent years, the rapid development of artificial intelligence technologies such as deep learning, knowledge graphs, and artificial neural networks has driven society from “Internet +” into a new era of “artificial intelligence +”
The “Education Information 2.0 Action Plan” emphasizes on establishing and improving the sustainable development mechanism of educational information and building a networked, digital, intelligent, personalized, and lifelong education system [1]. e key to building a new education system lies in the development of personalized learning, and the realization of personalized learning is inseparable from the strong support of the adaptive learning system. e core components of the adaptive learning system include five parts: domain model, user model, adaptive model, adaptive engine, and presentation model [2]. e domain model is an important foundation and core element of building an adaptive learning system, and building a domain model with clear semantics, complete structure, and good scalability is an important challenge faced by adaptive learning systems
In the e-learning environment, learners have different attributes, such as learning motivation, learning level, learning style, and preference, and these learner characteristics will affect the learner’s learning. e general recommendation technology limits the performance of the online learning recommendation system due to cold start and sparsity issues [7,8,9]; that is, when there are new learning resources that have not been evaluated or new users who have not commented on any items, it will lead to the inability to make accurate recommendations. e data are huge, and the acquisition of these data will become very sparse [10]
Summary
The rapid development of artificial intelligence technologies such as deep learning, knowledge graphs, and artificial neural networks has driven society from “Internet +” into a new era of “artificial intelligence +”. E artificial intelligence technology represented by the knowledge graph provides a technical guarantee for the construction of the educational field model. To solve the above problems, this paper mainly studies the recommendation algorithm based on knowledge representation and learning resources. E education domain model construction refers to the process of using knowledge extraction, knowledge fusion, and other technologies to establish connections between subject knowledge and knowledge. E concept map was first proposed by Professor Novak to organize and characterize knowledge It includes two parts, nodes and connections.
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