In the recent years, the ways people acquire information have been completely changed. Activitiesrnsuch as reading hardcopy materials such as books, journals, and newspapers, have radicallyrndeclined, and most of the people go online to find recent and up-to-date information. As a result,rnnews feeds technology such as RSS and ATOM was created to allow news users to get frequentlyrnupdate information. However, the number of news items that will be downloaded to the aggregatorrnwill be unmanageable when the number of provides grows. This will be even annoying when somernof the news items are similar to already read news items.rnOne of the possible solutions to this challenge is to measure similarity among news items. Measurernsimilarity between news items is pre-requisite to a number of application areas, grouping,rnclustering, merging and revision/version control. Since news Feeds are XML files, they do havernseveral sub-elements such as title, description/summery, link, guild, etcâ€¦. Previously item/entryrnsub-elements such as title and description/summary have been used as input in measuringrnsimilarity. In this work, we propose to use link sub-element information that improves andrnsupplement the similarity computation between two items. As news page contains links to set ofrnrelated news pages, our new similarity approach uses these links in measuring similarity. Werndeveloped new similarity measures that consider the link sub-element and related news linksrntogether with their anchor text.rnIn order to validate our approach, we developed a prototype implementing the link based newsrnFeed similarity measure. Experimental results show that the link based news feed similarity isrnmore helpful in measuring similarity when it is combined with computing similarity only withrntitle and description sub-elements and compared to using SimRank and co-citation.rnKeywords: similarity measure, link analysis, news Feed, Semantic similarity