Abstract: 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 K-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 K-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 "user-news category-news" 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 K-means clustering recommendation method, and the results show that the proposed method is feasible, and the accuracy rate, recall rate and F value all have been improved.

Key words: personalized recommendation, vector space model, Bisecting K-means clustering algorithm, user interest model