基于VSM和Bisecting K-means聚类的新闻推荐方法

A News Recommendation Method Based on VSM and Bisecting K-means Clustering

针对海量新闻数据给用户带来的困扰,为提升用户阅读新闻的个性化体验,提出了融合向量空间模型和Bisecting K-means聚类的新闻推荐方法.首先进行新闻文本向量化,使用向量空间模型和TF-IDF算法构建出新闻特征向量;采用Bisecting K-means聚类算法对新闻特征向量集进行聚类;然后将已聚类的新闻集分为训练集和测试集,根据训练集构建"用户—新闻类别—新闻"三层层次结构的用户兴趣模型;最后采用余弦相似度方法得出新闻推荐结果,并与测试集进行对比分析.实验以基于用户的协同过滤算法、基于物品的协同过滤算法、结合向量空间模型和K-means聚类的推荐方法为基准,实验结果表明,该方法具有可行性,在准确率、召回率和F值上都有所提高.

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 userbased 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.