Personalized Recommendation System Based On Reading Habit Characteristics

With the era of big data,especially the advent of "Internet +",traditional industries are gradually transforming into data-drived companies,and the value of data is becoming increasingly crusial.Academia has made a lot of attempts in the informationization of education.Many forms of education have emerged in the wave of informatization.Some experimental education projects being carried out,such as distance education,smart education,flipped classrooms,MOOCs,e-schoolbags,etc.,are all attempts to seek educational changes.This article starts with a recommendation system,analyzes and lists a series of recommendation algorithms.We start with reading behavior data,and try to construct students' high-level reading behaviors as teaching aids.At the same time,the development of some big data platforms is analyzed.Based on the Spark big data platform,the big data system in this paper has designed a set of big data recommendation system that integrates data storage,data cleaning,and personalized recommendation.Based on multi-level behavioral characteristics of reading behavior data modeling and user portrait generation,this paper proposes a collaborative thresholding algorithm based on fixed threshold neighbors based on the collaborative filtering algorithm of neighbors.This paper has verified the effectiveness of the above scheme through a lot of experiments.Existing data sets are compared horizontally and vertically to obtain the best models and parameters.The results show that the personalized recommendation system based on the characteristics of individual reading habits proposed in this paper has practical significance,can reasonably and effectively use the reading behavior data of students,and accurately locate the reading habits and reading styles of students,while giving appropriate recommendations.