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Optimizing an English text reading recommendation model by integrating collaborative filtering algorithm and FastText

Ke Yan1

  • 1Department of Public Instruction, Nanyang Medical College, Nanyang, 473000, Henan, China.

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Summary
This summary is machine-generated.

This study introduces an English text recommendation model for long-tail users, integrating collaborative filtering and FastText classification. The model significantly improves recommendation accuracy and user satisfaction by analyzing reading behaviors and interests.

Keywords:
Collaborative filtering algorithm integrationEnglish textF-measureFastText classification methodRecommendation accuracy

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Area of Science:

  • Information Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Understanding diverse user reading habits is crucial for effective content recommendation.
  • Long-tail users often have unique preferences not adequately addressed by traditional models.
  • Tailored reading recommendations enhance user engagement and satisfaction.

Purpose of the Study:

  • To develop and evaluate an English text reading recommendation model for long-tail users.
  • To integrate collaborative filtering with FastText classification for improved recommendation accuracy.
  • To enhance user satisfaction through personalized reading suggestions.

Main Methods:

  • Integration of collaborative filtering algorithms with the FastText classification method.
  • Calculation of user interest distribution using an enhanced Ebbinghaus forgetting curve and reading behavior analysis.
  • Amalgamation of collaborative filtering with association rule-based algorithms for recommendation generation.
  • Comparative analysis against Top-N, matrix factorization, and FastText models.

Main Results:

  • The proposed model demonstrated superior recommendation accuracy, achieving up to 0.80 for a list of 50 texts.
  • The F-Measure reached 0.81, significantly outperforming other evaluated algorithms.
  • The model showed commendable performance in recall rate, RMSE, NCG, precision, and accuracy.
  • The system effectively reflects user reading interests, enhancing overall performance.

Conclusions:

  • The developed English text recommendation model effectively caters to long-tail users.
  • The integration of collaborative filtering and FastText significantly boosts recommendation accuracy and system efficacy.
  • The findings provide valuable guidance for improving English text recommendation systems.