%0 Journal Article
%A CHEN Gang
%A LI Feng
%A WEI Shuang-jian
%A YUAN Ren-jin
%T A News Recommendation Method Based on VSM and Bisecting <em>K</em>-means Clustering
%D 2019
%R 10.13190/j.jbupt.2018-067
%J Journal of Beijing University of Posts and Telecommunications
%P 114-119
%V 42
%N 1
%X Personalized recommendation technology is a good solution to the problem of information overload. In order to improve the user's personalized experience of reading news, a news recommendation method based on the vector space model and Bisecting <em>K</em>-means clustering is proposed. Firstly, the news text vectorization is carried out:using the vector space model and TF-IDF algorithm to construct news feature vectors; then Bisecting <em>K</em>-means clustering algorithm is utilized to cluster the news feature vector set; after that, the clustered news set is divided into training set and test set, according to the training set, a &quot;user-news category-news&quot; three-level structure of the user interest model is built; finally, the cosine similarity method is used to calculate news recommendation results. The experiments are based on user-based collaborative filtering algorithm, item-based collaborative filtering algorithm, combined vector space model and <em>K</em>-means clustering recommendation method, and the results show that the proposed method is feasible, and the accuracy rate, recall rate and <em>F</em> value all have been improved.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-067