Social net: using patterns of physical proximity over time to infer shared interests
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Social Serendipity: Mobilizing Social Software
IEEE Pervasive Computing
P3 Systems: Putting the Place Back into Social Networks
IEEE Internet Computing
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
MobiSoC: a middleware for mobile social computing applications
Mobile Networks and Applications
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards an understanding of social inference opportunities in social computing
Proceedings of the 17th ACM international conference on Supporting group work
Is there a place for serendipitous introductions?
Proceedings of the companion publication of the 17th ACM conference on Computer supported cooperative work & social computing
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Social matching systems recommend people to other people. With the widespread adoption of smartphones, mobile social matching systems could potentially transform our social landscape. However, we have a limited understanding of what makes a good social match in the mobile context. We present a theoretical framework which outlines how a user's context and the rarity of different affinity measures in various contexts (match rarity) can be used to provide valuable social matches. We suggest that if a user attribute is very rare in a particular context, users will generally be more interested in an affinity match. We conducted a survey study to assess this framework with 117 respondents. We found that both context and match rarity significantly influence interest in a social match. These results validate the key aspects of the framework. We discuss the results in terms of implications for social matching system design.