FastText classification

Citation Author(s):
Ke
Yan
Submitted by:
Ke Yan
Last updated:
Thu, 12/21/2023 - 20:24
DOI:
10.21227/3k9r-ws11
Data Format:
*.csv
License:
Creative Commons Attribution
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Categories:
Education and Learning Technologies
Keywords:
FastText classification
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Abstract 

The CF algorithm is combined to generate personalized English text reading recommendations for various long-tail user groups. By optimizing the recommendation generation process, the recommendation accuracy of the model is enhanced, and the recommendation performance and user satisfaction of the English text reading recommendation system are improved. The Top-N algorithm model is compared with the algorithm model based on matrix decomposition in terms of recommendation accuracy and F-Measure value, and the advantages of the proposed algorithm model are proved.

Instructions: 

The CF algorithm is combined to generate personalized English text reading recommendations for various long-tail user groups. By optimizing the recommendation generation process, the recommendation accuracy of the model is enhanced, and the recommendation performance and user satisfaction of the English text reading recommendation system are improved. The Top-N algorithm model is compared with the algorithm model based on matrix decomposition in terms of recommendation accuracy and F-Measure value, and the advantages of the proposed algorithm model are proved.

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