Supporting Opportunities for Context-Aware Social Matching: An Experience
Sampling Study
Contextual Awareness
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Mayer, Julia M.
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Hiltz, Starr Roxanne
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Barkhuus, Louise
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Väänänen, Kaisa
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Jones, Quentin
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.2430-2441
© Copyright 2016 ACM
Summary: Mobile social matching systems aim to bring people together in the physical
world by recommending people nearby to each other. Going beyond simple
similarity and proximity matching mechanisms, we explore a proposed framework
of relational, social and personal context as predictors of match opportunities
to map out the design space of opportunistic social matching systems. We
contribute insights gained from a study combining Experience Sampling Method
(ESM) with 85 students of a U.S. university and interviews with 15 of these
participants. A generalized linear mixed model analysis (n=1704) showed that
personal context (mood and busyness) as well as sociability of others nearby
are the strongest predictors of contextual match interest. Participant
interviews suggest operationalizing relational context using social network
rarity and discoverable rarity, and incorporating skill level and
learning/teaching needs for activity partnering. Based on these findings we
propose passive context-awareness for opportunistic social matching.