Survey on the Current Situation of College English Teaching in China’s Universities and the Direction of College English Teaching Reform and Development

Abstract

With the development of internationalization, English learning becomes more and more important. In the process of language learning, writing has always played a very important role. A writer’s language proficiency can be improved by the amount of reading experience and knowledge, which is necessary to produce high-quality writing. In recent years, there have been many writing assistant recommendation systems supported by different technical means, which provide great help for college students’ writing. In order to solve the problem that traditional recommendation algorithms can not recommend accurately, this paper proposes a hybrid recommendation algorithm and applies it to the recommendation of English writing documents. The algorithm generates three-dimensional feature vectors by learning the characteristics of students like, dislike, and similar students. Three low-dimensional feature vectors are linearly combined to form the representation vector of college students. And the cosine similarity is used as the similarity index to recommend English writing literature related to similar college students to the target college students, so as to achieve the recommendation of English writing literature. Experimental results show that this recommendation algorithm is superior to the other four algorithms in mean absolute error (MAE) and time performance and has high recommendation quality.