CSR: Small: Efficient and Scalable Systems Support for Mobile Group Formation, Inference, Recommendation and Classification

University Of Colorado At Boulder, Boulder CO

Investigators

Abstract

Users of smartphones are increasingly using their mobile devices to organize activities as mobile groups of friends, family, and colleagues. In order to improve the richness of interaction within such mobile groups, this research project investigates the creation of new mobile group services and the development of a practical framework that will enable such novel capabilities as providing recommendations to mobile groups of people with diverse interests. This project proposes to develop novel ways to identify when cyberbullying by a group of users is occurring in mobile social networks. In addition, since there are millions of groups of users who use social media every day to communicate, this project will investigate how recommendation and identification algorithms for mobile groups of users can be made to scale so they can quickly provide accurate recommendations to such groups of users, and rapidly identify mobile group cyberbullying behavior. This project's research and its intellectual merit focuses on advancing the state of the art in understanding mobile group dynamics, creating accurate recommendation and classification algorithms for mobile groups of users, and developing scalable infrastructure upon which new mobile group applications can be deployed. The research plan is driven by two novel and representative case studies, namely mobile group recommendation and cyberbullying detection in mobile groups. The project will generate an integrated systems framework consisting of six major components to support mobile group applications: mobile client apps; a set of core mobile group services including voting, multicast communication, rating, and role definition; a collection of mobile group information; a mobile group inference engine; a mobile group recommendation and prediction engine; and a mobile group classification engine. Society as a whole may strongly benefit from new mobile group services and algorithms for mobile group recommendation and classification developed within this project, thereby enabling mobile groups of users to more easily and richly interact. Further, the research community will benefit by the creation of open source infrastructure that can stimulate further research on mobile groups of users. The project will produce STEM educational materials for middle/high school students, recruit undergraduates and underrepresented women and minorities for research, integrate the results of the research into classes taught by the investigators, and strengthen interdisciplinary research.