Algorithm for Ranking News

摘要：

With the overwhelming volume of online news available today, there is an increasing need for efficient technique to satisfy user news need. In this paper, news ranking is discussed and news informational retrieval model is presented for novel news ranking algorithm. In terms of examination of properties of news articles produced by news ranking function, semantic relevancy, freshness, citation count and degree of authority are combined into the model, and extended relevance is proposed. The basic idea is that the relevance between news article and user news need is determined by semantic relevance, freshness, citation count and degree of authority of news article. The experimental results show that new model and algorithm have higher precision and produce more relevant results than traditional vector retrieval model.

作者: LIU Xiaofeng CHEN Chuanbo LIU YunSheng

作者单位: School of Software Engineering, Huazhong University of Science and Technology

会议类型: 国际会议

会议名称: 2007年第三届语义和知识网格国际会议(Third International Conference on Semantics,Knowledge,and Grid)(SKG 2007)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-10-29（万方平台首次上网日期，不代表论文的发表时间）