Common attributes in an unusual context: Predicting the desirability of a social match

Julia M. Mayer, Sara Motahari, Richard P. Schuler, Quentin Jones

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationRecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems
Pages337-340
Number of pages4
DOIs
StatePublished - Dec 15 2010
Event4th ACM Recommender Systems Conference, RecSys 2010 - Barcelona, Spain
Duration: Sep 26 2010Sep 30 2010

Publication series

NameRecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems

Other

Other4th ACM Recommender Systems Conference, RecSys 2010
Country/TerritorySpain
CityBarcelona
Period9/26/109/30/10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Software
  • Control and Systems Engineering

Keywords

  • Context- aware computing
  • Mobile social matching
  • People recommendations

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