Algorithm for Ranking News

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.