Proceedings of the 2nd International Conference on Internet Technology and Educational Informatization, ITEI 2022, December 23-25, 2022, Harbin, China

Research Article

Research on English Vocabulary Learning Platform Based on Personalized Recommendation

Download137 downloads
  • @INPROCEEDINGS{10.4108/eai.23-12-2022.2329214,
        author={Hongyan  Cai},
        title={Research on English Vocabulary Learning Platform Based on Personalized Recommendation},
        proceedings={Proceedings of the 2nd International Conference on Internet Technology and Educational Informatization, ITEI 2022, December 23-25, 2022, Harbin, China},
        publisher={EAI},
        proceedings_a={ITEI},
        year={2023},
        month={6},
        keywords={personalized recommendation; spatial model; collaborative filtering; lexical recommendation; dynamic model},
        doi={10.4108/eai.23-12-2022.2329214}
    }
    
  • Hongyan Cai
    Year: 2023
    Research on English Vocabulary Learning Platform Based on Personalized Recommendation
    ITEI
    EAI
    DOI: 10.4108/eai.23-12-2022.2329214
Hongyan Cai1,*
  • 1: Shandong Agriculture and Engineering University
*Contact email: 9903205@qq.com

Abstract

In the mobile learning environment, it is necessary to consider how to mine learners' personalized vocabulary needs and recommend appropriate vocabulary resources according to the characteristics of resources and learners. This is of practical significance for improving learners' vocabulary learning efficiency. Based on the classification time perception method and vector space model representation, this paper constructs the learner dynamic interest model. This model can represent learners' personalized features more accurately and improve the accuracy of word recommendation. Combined with the collaborative filtering recommendation based on learners' dynamic interests, it can recommend vocabulary resources for learners. Finally, through the questionnaire survey, the effectiveness of the system is tested from the aspect of the learning effect of the system. The data show that the system can improve the user's learning effect to a certain extent.