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Design and Implementation of English Teaching Resources Retrieval Algorithm Model Based on Deep Learning

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DOI: 10.23977/acss.2022.060309 | Downloads: 12 | Views: 582

Author(s)

Cheng Huang 1, Rulin Chen 1, Ling Lin 1

Affiliation(s)

1 Hainan Vocational University of Science and Technology, Haikou, Hainan, 571126, China

Corresponding Author

Cheng Huang

ABSTRACT

With the development of computer network and information technology, the requirements for educational environment have been further improved. At present, there are web-based examination systems in almost every field. Especially English learning, because students go from primary school to university, and even study abroad, English plays an important role in all kinds of examinations, and for this reason, all kinds of online learning have appeared. However, almost all English learning systems cannot improve students' interest and efficiency. Through the analysis and comparison of the matching degree between learners' interest and resources, an algorithm model for realizing accurate resource retrieval is put forward and verified in related systems. At the same time, this algorithm has a good reference value for realizing personalized recommendation of resources. With the emergence of various resource information bases, it is an urgent problem for every resource information provider to find the resources that users are interested in from the massive resource information bases. In this paper, we propose an algorithm to achieve accurate resource retrieval by comparing the matching degree between user interest model and information resource model, and implement it in related systems. The algorithm also has a good reference value for personalized recommendation of resource information.

KEYWORDS

Deep learning, English teaching, Resource retrieval algorithm, Model design and implementation

CITE THIS PAPER

Cheng Huang, Rulin Chen, Ling Lin, Design and Implementation of English Teaching Resources Retrieval Algorithm Model Based on Deep Learning. Advances in Computer, Signals and Systems (2022) Vol. 6: 72-78. DOI: http://dx.doi.org/10.23977/acss.2022.060309.

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