@inproceedings{1c9f3b6eb7a4471eb532693c70b41c2f,
title = "Supporting Opportunities for Context-Aware Social Matching: An Experience Sampling Study",
abstract = "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.",
keywords = "Mobile Social Matching Systems, Opportunistic Social Matching, Personal Context, Relational Context, Experience Sampling Method, Mobile Social Matching Systems, Opportunistic Social Matching, Personal Context, Relational Context, Experience Sampling Method",
author = "Julia Mayer and Louise Barkhuus and Hiltz, {Starr Roxanne} and Kaisa Vaananen and Quentin Jones",
year = "2016",
month = may,
doi = "10.1145/2858036.2858175",
language = "English",
isbn = "978-1-4503-3362-7",
series = "ACM Annual Conference on Human Factors in Computing Systems (CHI)",
publisher = "Association for Computing Machinery",
pages = "2430--2441",
booktitle = "Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems",
address = "United States",
}