A news recommendation method based on two-fold clustering

作者：Gu, Wan-Rong; Dong, Shou-Bin; He, Jin-Chao; Zeng, Zhi-Zhao

来源：Journal of South China University of Technology(Natural Science Edition), 2014, 42(7): 15-20 and 32.

DOI：10.3969/j.issn.1000-565X.2014.07.003

• Cluster numbers
• Computation costs
• Density clustering
• Initial cluster centers
• News recommendation
• Personalized recommendation
• Text Clustering
• Traditional clustering

Due to fast update of news, the clustering-based preprocessing is usually needed when the news is recommended to users. However, some traditional clustering methods are too complicated while others rely on iterative initial value, none of which can be accurately and effectively applied to news recommendation. Considering the above issues, we propose a news recommendation method based on two-fold clustering. In this method, a density clustering of random sample data is conducted. Based on the cluster number and initial cluster center of the density clustering, a fast two-fold clustering of all the news to be recommended is performed. Then, the news recommendation is realized by combining such factors as fashionability and popularity. The proposed method can cluster relevant news without too much computation cost, and it can calculate parameters by means of parameter estimation. Experimental results show that the proposed method is superior to other news recommendation methods in terms of diversity and accuracy.

• 出版日期2014-7-1
• 单位华南理工大学