A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

Yan Tang,Yang Yu,Jian Su

doi:10.1155/2022/7077123

Abstract

In order to ensure the quality of resource recommendation and solve the problems of low recommendation accuracy, long recommendation time, and high data loss rate in the process of resource recommendation in traditional methods, a personalized recommendation system of English teaching resources based on the multi-K nearest neighbor regression algorithm is designed. According to the overall architecture of the personalized recommendation system of teaching resources, this study designs the resource browsing function module, teaching resource detailed page recommendation module, and teaching resource database. Based on the basic idea of the multi-K nearest neighbor regression algorithm, in order to avoid the loss of important data in English teaching resource recommendation and reduce the data loss rate, a missing data reconstruction algorithm of English teaching resources is proposed. Finally, the path interest of student users is considered from the selection of browsing path and access time to realize the personalized recommendation of English teaching resources. The experimental results show that the system has high resource recommendation accuracy, short recommendation time, and low data loss rate.

Highlights

Network teaching has become a new learning mode, but there are still many defects in the current network learning resource system: there are so many learning resources that learners cannot find the required resources quickly like looking for a needle in a haystack; learners need to manually input descriptive words for search, and the system cannot actively recommend according to the user’s own information [1]. ese defects make network teaching lose its original advantages. erefore, it is urgent to integrate personalized services into network teaching [2]
Reference [5] proposes an online learning resource recommendation method based on ontology and cyclic neural network
To solve the problems of low recommendation accuracy, long recommendation time, and high data loss rate in the resource recommendation process of the above traditional methods, a personalized recommendation system of English teaching resources based on the multi-K nearest neighbor regression algorithm is designed. e main innovations of the system are as follows: (1) e overall architecture of the personalized recommendation system of teaching resources is designed. e component modules of this architecture are as follows: consulting module, transmission module, scoring and comment function module, resource management function module, message reminder function module, and teaching resource detailed page recommendation module

Summary

Introduction

Network teaching has become a new learning mode, but there are still many defects in the current network learning resource system: there are so many learning resources that learners cannot find the required resources quickly like looking for a needle in a haystack; learners need to manually input descriptive words for search, and the system cannot actively recommend according to the user’s own information [1]. ese defects make network teaching lose its original advantages. erefore, it is urgent to integrate personalized services into network teaching [2]. Reference [5] proposes an online learning resource recommendation method based on ontology and cyclic neural network. Reference [7] proposes a personalized learning resource recommendation method based on three-dimensional feature collaborative domination. E advantages of content-based recommendation lie in the user independence, the interpretability of recommendation results brought by high project transparency, and the ability of new projects to enter the recommendation list It has some problems, such as the acquisition of domain knowledge, limited analyzable content, and obsolete recommended content. To solve the problems of low recommendation accuracy, long recommendation time, and high data loss rate in the resource recommendation process of the above traditional methods, a personalized recommendation system of English teaching resources based on the multi-K nearest neighbor regression algorithm is designed. E proposed method is presented in Section 3, and in Section 4, we illustrated the experimental information and the results of the experiments; Section 5 provides the conclusion

Design of a Personalized Recommendation System for Teaching Resources

Personalized Recommendation Algorithm for English Teaching Resources

Experimental Design

Findings

Conclusion