A personalised push method of English mobile reading resources based on tag similarity

Aiming at shortening the recommendation time of reading resources and improving the retention rate of recommendation results, a new personalised push method of English mobile reading resources was studied based on label similarity. Firstly, preprocess resource data by data cleaning and noise reduction. K-means clustering algorithm is used to cluster resource data. Secondly, calculate the frequency of resource tags used by users according to their implicit needs and preferences. Finally, the cosine similarity between user tags and resource tags in the same category is obtained, and a push list is formed to push resources before ranking test for users. The experimental results show that the maximum generation time of the push list is only 4,372 ms after the application of this method, and the retention rate of the recommendation results is always above 93%.