Abstract
In the recent, the World Wide Web has become a platform for online news publications. Many sources started publishing digital versions of news articles online to vast users through a variety of devices, i.e. television channels, magazines, and newspapers. It is observed that the news articles available can be very huge and recommendation systems can help to recommend relevant news to the news readers by filtering news articles based on some predefined criteria or similarity measure, i.e. collaborative filtering or content-based filtering approach. The paper presents named entities based similarity measure for linking digital news stories published in various newspapers during the preservation process in a digital news stories archive to ensure future accessibility. The study compares the similarity of news articles based on human judgment with a similarity value computed automatically using the proposed technique. The results are generalized by defining a threshold value based on multiple experimental results using different datasets of different size.
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