Recommendation of English Reading in Vocational Colleges Using Linear Regression Training Model

Abstract

High-quality vocational education is a vital basis for China's efforts to develop high-quality teachers and technical and skilled workers. The increasing development of contemporary vocational education is given higher importance in the new era. We proposed the stick to moral education while carefully integrating it with the requirements of changes in technology and modernization, continuous improvements in the quality of college vocational education, and the development of more conceptual and skilled talented individuals with both political integrity and the capacity to build modernization. Using artificial intelligence technology to challenge these difficulties in the context of social informatization can play important role in enhancing the teaching quality of high vocational education. With the advent of the Internet era, students and parents have an increasingly strong demand for online independent vocational English reading materials, although there is some English learning software that provides online vocational English reading materials in the education market. However, most of this software only changes the reading form of paper vocational English reading materials but fails to completely meet the real demand of students for vocational English reading materials, so how to carry out personalized recommendation reading according to the real reading situation and reading preferences of students is a vital problem to be resolved. In this background, this paper studies the personalized recommendation technology, in the use of recommendation algorithms on the based association rules to obtain higher vocational English reading content recommended the strongest association rules, on the based combination of user-based collaborative clarifying recommendation algorithm. The linear regression model combined with the users reading interest and personalized recommendation of higher vocational English reading process is optimized, improving the accuracy of English reading recommendation in vocational colleges.