ABSTRACT
To comprehend the genuine reading habits and preferences of diverse user cohorts and furnish tailored reading recommendations, this study introduces an English text reading recommendation model designed specifically for long-tail users. This model integrates collaborative filtering algorithms with the FastText classification method. Initially, the integrated collaborative filtering algorithm is explicated, followed by the calculation of the user's interest distribution across various types of English texts, achieved through an enhanced Ebbinghaus forgetting curve and analysis of user reading behaviors. Subsequently, an intelligent English text reading recommendation is generated by amalgamating collaborative filtering algorithms with association rule-based recommendation algorithms. Through optimization of the recommendation generation process, the model's recommendation accuracy is enhanced, thereby augmenting the performance and user satisfaction of the recommendation system. Finally, a comparative analysis is conducted with respect to the Top-N algorithm model, matrix factorization-based algorithm model, and FastText classification model, illustrating the superior recommendation accuracy and F-Measure value of the proposed model. The study findings indicate that when the recommendation list contains 10, 30, 50, and 70 texts, the recommendation accuracy of the proposed algorithm model is 0.75, 0.79, 0.8, and 0.74, respectively, outperforming other algorithms. Furthermore, as the number of texts increases, the F-Measure of all four models gradually improves, with the final F-Measure of the proposed model reaching 0.81. Notably, the F-Measure of the English text reading recommendation model proposed in this study significantly surpasses that of the other three recommendation methods. Demonstrating commendable performance in recall rate, root mean square error, normalized cumulative gain, precision, and accuracy, the model adeptly reflects user reading interests, thereby enhancing the accuracy of text recommendations and the overall system performance. The study findings offer crucial insights and guidance for enhancing the accuracy and overall efficacy of English text recommendation systems.
ABSTRACT
Las tecnologías de la información y la comunicación, ofrecen un universo ilimitado de posibilidades como recurso para el aprendizaje y socialización del conocimiento; la educación no ha escapado a ello. La revolución informática demanda nuevos retos, uno de ellos lo constituye la enseñanza de la lectura en idioma inglés. El sitio web KidsHealth, tiene potencialidades para trabajar la comprensión de textos en inglés y consolidar la formación vocacional hacia las ciencias médicas. Las filiales médicas de salud, están estrechamente vinculadas, mediante el proceso de extensión universitaria, a la consolidación de la formación vocacional. El objetivo del presente trabajo es divulgar y promocionar las ventajas y potencialidades de este sitio Web para la comprensión de textos en inglés y la formación vocacional hacia las ciencias médicas...
The information and communication technologies offer unlimited possibilities as a resource of learning and knowledge socialization, and education is not exempted from this reality. The informational revolution demands new challenges; one of them is the teaching of English reading. Website known as KidsHealth has potentialities to develop the understanding of English texts and to consolidate the vocational formation of students for the medical sciences. The universitarian medical sites are closely related to the consolidation of the vocational process through the process of university extension program. The objective of the present paper was to disseminate and to promote the advantages and the potentialities of this Website for the comprehension of English texts and the vocational formation in medical sciences...