Machine Learning Approach to Evaluate News Search Engines

News search engines are the specialized search services devoted for searching news online. Reading news using hardcopy print news papers has now become old fashioned and people prefers online news reading. News search engines like Google news and Bing news are available which facilitates the news search. In this paper we propose a machine learning approach to compare Google news and Bing news in terms of retrieval effectiveness both considering sole relevancy parameters as well as incorporating freshness with the relevance. Experimental results show that Bing news is better for relevance retrieval and Google news have the higher average NDCG value when we incorporates freshness also with the sole relevance parameters.