18,908 research outputs found
Oral History Interview with Pang Yang Hoong: Conceptualising SMU
This is an abridged version of the original interview. Please contact the Library at [email protected] for access to the full version of the transcript and/or audio recording.</p
DeepCity: A Feature Learning Framework for Mining Location Check-ins
Online social networks being extended to geographical space has resulted in
large amount of user check-in data. Understanding check-ins can help to build
appealing applications, such as location recommendation. In this paper, we
propose DeepCity, a feature learning framework based on deep learning, to
profile users and locations, with respect to user demographic and location
category prediction. Both of the predictions are essential for social network
companies to increase user engagement. The key contribution of DeepCity is the
proposal of task-specific random walk which uses the location and user
properties to guide the feature learning to be specific to each prediction
task. Experiments conducted on 42M check-ins in three cities collected from
Instagram have shown that DeepCity achieves a superior performance and
outperforms other baseline models significantly
Location Prediction: Communities Speak Louder than Friends
Humans are social animals, they interact with different communities of
friends to conduct different activities. The literature shows that human
mobility is constrained by their social relations. In this paper, we
investigate the social impact of a person's communities on his mobility,
instead of all friends from his online social networks. This study can be
particularly useful, as certain social behaviors are influenced by specific
communities but not all friends. To achieve our goal, we first develop a
measure to characterize a person's social diversity, which we term `community
entropy'. Through analysis of two real-life datasets, we demonstrate that a
person's mobility is influenced only by a small fraction of his communities and
the influence depends on the social contexts of the communities. We then
exploit machine learning techniques to predict users' future movement based on
their communities' information. Extensive experiments demonstrate the
prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201
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