Linking people through physical proximity in a conference
Proceedings of the 3rd international workshop on Modeling social media
Mining social relationship types in an organization using communication patterns
Proceedings of the 2013 conference on Computer supported cooperative work
Connecting people through physical proximity and physical resources at a conference
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
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In online social networks such as Face book, people receive friend recommendations that are based usually on common friends or similar profile such as having the same interest or coming from the same company. However, people receive friend spam in which they do not know why they should add this friend. If we can record the physical context then we can determine how you met that person, and use that for recommending that person to you. In this paper, we create a friend recommendation system using proximity encounters and meetings as physical context called Encounter Meet. We conduct a user study to examine whether physical context-based friend recommendations is better than common friends. Results show that on average, the Encounter Meet algorithm recommended more friends to participants that they added and more recommendations were ranked as good, compared with the common friend algorithm. The results can be used to help design context-aware recommendations in physical environments.