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FriendSensing: recommending friends using mobile phones

Quercia, D.; Capra, L.; (2009) FriendSensing: recommending friends using mobile phones. In: RecSys' 09: Proceedings of the third ACM conference on Recommender systems. (pp. pp. 273-276). Association for Computing Machinery: New York, US. Green open access

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Abstract

Social-networking sites, such as Facebook, require members to manually find and confirm their friends. Finding friends is tedious for some and may be made less so by automating the process. We propose to do so by means of a framework that we call FriendSensing. Using short-range technologies (e.g., Bluetooth) on their mobile phones, social-networking users “sense” and keep track of other phones in their proximity. Proximity records are then processed using a variety of algorithms that are based on social network theories of geographical proximity and of link prediction. This processing can be performed either on the social-networking website, after records have been uploaded, or locally on the user’s mobile phone, so that privacy-conscious individuals do not have to disclose their proximity data to the social networking website. The result is a personalized and automatically generated list of people the user may know. We evaluate the extent to which FriendSensing helps users find people they know, and we do so against real mobility and social network data.

Type: Proceedings paper
Title: FriendSensing: recommending friends using mobile phones
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/1639714.1639766
Publisher version: http://doi.acm.org/10.1145/1639714.1639766
Language: English
Additional information: © ACM, 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in RecSys' 09: Proceedings of the third ACM conference on Recommender systems, (2009) http://doi.acm.org/10.1145/1639714.1639766
URI: https://discovery-pp.ucl.ac.uk/id/eprint/16835
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