Design And Implementation Of English Picture Book Reading Recommendation System Based On Forgetting Curve

With the implementation of quality education and the reform of primary school English education and teaching.With its unique auxiliary teaching features,English picture books(A kind of storybook based on children's psychological characteristics and cognitive development level,which is mainly based on pictures and supplemented by words)have received extensive attention in the classroom teaching inside and outside in the primary schools.The advent of the Internet era has spurred the increasing demand for online and autonomous reading of picture books by students and parents.There are some English learning softwires that provide online reading functions on the education market,most of these softwires simply change the reading form of paper-based picture books,but do not completely satisfy the students' real needs for reading.So how to personally recommend reading for students' real reading and reading preferences is an urgent problem to be solved.In this context,based on the research of personalized recommendation technology,based on the recommendation algorithm based on association rules to obtain the strongest association rules for picture book reading recommendation,combined with the user-based collaborative filtering recommendation algorithm,the Ebbinghaus forgetting curve is the same.The user's reading interest is combined and improved,and the personalized recommendation process of the picture book reading is optimized to improve the accuracy of the picture book reading recommendation.In the English picture book reading recommendation system based on Ebbinghaus's forgetting curve designed and implemented in this paper,the combination of Ebbinghaus's forgetting curve and user's picture book reading interest and the rapid classification of English text are the core.Therefore,the user's behavior weights of the picture book are compared to the memory level of the person in the forgetting curve.The retention quantity function is used to obtain the student's reading interest memory retention rate and the corresponding interest proportion of the different types of picture books(This article uses the fast text classification method FastText to classify English picture books).On this basis,the recommendation algorithm based on association rules is used to process the strongest association rules for picture book reading and the user-based collaborative filtering recommendation algorithm is used to process the set of pictures to be recommended for users with the same reading interest,and then processed by linear regression model.The optimal recommendation ratios under the above two recommendation algorithms are obtained,and the product is compared with the proportion of interest categories of the picture books,and the final recommend ability of the picture books is obtained,which is arranged in descending order,and the recommendation scheme is optimized in time.Finally,the optimized picture book reading recommendation scheme is applied to the specific recommendation system according to the Top-N criterion.In the aspect of experimental verification,this paper verifies the picture reading recommendation system from the recommendation performance.The actual results show that the proposed personalized recommendation scheme for English picture book based on Ebbinghaus's forgetting curve is feasible,and the accuracy and recall are Rate and F-Measure and other indicators also showed better performance.