Supporting Opportunities for Context-Aware Social Matching: An Experience Sampling Study

Julia Mayer, Louise Barkhuus, Starr Roxanne Hiltz, Kaisa Vaananen, Quentin Jones

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 2016 CHI Conference on Human Factors in Computing Systems
Number of pages12
PublisherAssociation for Computing Machinery
Publication dateMay 2016
ISBN (Print)978-1-4503-3362-7
Publication statusPublished - May 2016
SeriesACM Annual Conference on Human Factors in Computing Systems (CHI)


  • Mobile Social Matching Systems
  • Opportunistic Social Matching
  • Personal Context
  • Relational Context
  • Experience Sampling Method


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