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Discovering shared interests using graph analysis
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Referral Web: combining social networks and collaborative filtering
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Measuring similarity of interests for clustering web-users
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HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
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In this work we examine nine different sources for user similarity as reflected by activity in social media applications. We suggest a classification of these sources into three categories: people, things, and places. Lists of similar people returned by the nine sources are found to be highly different from each other as well as from the list of people the user is familiar with, suggesting that aggregation of sources may be valuable. Evaluation of the sources and their aggregates points at their usefulness across different scenarios, such as information discovery and expertise location, and also highlights sources and aggregates that are particularly valuable for inferring user similarity.