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Regroup: interactive machine learning for on-demand group creation in social networks

Published: 05 May 2012 Publication History

Abstract

We present ReGroup, a novel end-user interactive machine learning system for helping people create custom, on demand groups in online social networks. As a person adds members to a group, ReGroup iteratively learns a probabilistic model of group membership specific to that group. ReGroup then uses its currently learned model to suggest additional members and group characteristics for filtering. Our evaluation shows that ReGroup is effective for helping people create large and varied groups, whereas traditional methods (searching by name or selecting from an alphabetical list) are better suited for small groups whose members can be easily recalled by name. By facilitating on demand group creation, ReGroup can enable in-context sharing and potentially encourage better online privacy practices. In addition, applying interactive machine learning to social network group creation introduces several challenges for designing effective end-user interaction with machine learning. We identify these challenges and discuss how we address them in ReGroup.

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    cover image ACM Conferences
    CHI '12: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    May 2012
    3276 pages
    ISBN:9781450310154
    DOI:10.1145/2207676
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 05 May 2012

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    Author Tags

    1. example and feature-based interaction
    2. interactive machine learning
    3. social network access control

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