MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion With Mobile Crowd Sensing
This project presents a
group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world
settings. Acknowledging the complexity and diversity of group activities, this
paper introduces a formal concept model to characterize group activities and
classifies them into four organizational stages. We then present an intelligent
approach to support group activity preparation, including a heuristic
rule-based mechanism for advertising public activity and a context-based method
for private group formation. In addition, we leverage features extracted from
both online and offline communities to recommend ongoing events to attendees
with different needs.
Compared with the baseline method, people preferred
public activities suggested by our heuristic rule-based method. Using a dataset
collected from 45 participants, we found that the context based approach for
private group formation can attain a precision and recall of over 80%, and the
usage of spatial–temporal contexts and group computing can have more than a 30%
performance improvement over considering the interaction frequency between a user
and related groups. A case study revealed that, by extracting the features such
as dynamic intimacy and static intimacy, our cross-community approach for
ongoing event recommendation can meet different user needs.
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