Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering.

Gongwen Xu,Guangyu Jia,Zhijun Zhang,Lin Shi,Ahmed Mostafa Khalil

doi:10.1155/2021/9590502

Gongwen Xu, Guangyu Jia + Show 3 more

Open Access

https://doi.org/10.1155/2021/9590502

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Abstract

Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners' enthusiasm and give full play to different learners' learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.

Highlights

With the integration of Internet and education, various online education platforms have emerged and developed rapidly. ese platforms have accumulated many users with their high-quality and massive resources
(3) Cold start problem: the course platform would constantly update the content under the needs of new learners and new courses, but there is no record of new learners or new course content before updating the course platform, resulting in the failure of the recommendation algorithm to make timely and effective recommendations [19]
Representation learning aims to transform the objects into the low-dimensional space [26], and the aim of knowledge graph representation learning is to map the entities and relationships in the knowledge graph. e resulting vector can effectively represent the semantic relationship between entities and relationships [27]

Summary

Introduction

With the integration of Internet and education, various online education platforms have emerged and developed rapidly. ese platforms have accumulated many users with their high-quality and massive resources. Learners are usually short of an in-depth understanding of the overall knowledge structure, while the number of Internet learning resources is miscellaneous, so learners fall into a large number choices, resulting in information overload and even the low course passing rate [2, 3]. How to provide personalized content for learners in massive courses resources is a problem worthy of research. Aguilar et al analysed the differences and similarities between the course selection system and the e-commerce platform, improved knowledge, and discovered association rules through biological heuristic algorithms, while most of the research in recent years were to explore the behaviour characteristics of learners to represent the characteristics of learners, so as to produce recommendation results [14]. There have been a lot of works in learner personalized course recommendation recently years

Overview of the Course Recommendation Algorithm

Related Research

Experimental Process

Experimental Process and Result Analysis

Findings

Conclusions