THESIS
2020
ix, 57 pages : illustrations ; 30 cm
Abstract
As Location-Based Social Networks (LBSNs) have become widely used by users, understanding
user engagement and predicting user churn are essential to the maintainability
of the services. In this thesis, we conduct a quantitative analysis to understand user engagement
patterns exhibited both offline and online in LBSNs. We employ two large-scale
datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19
million tips in Yelp and Foursquare, respectively. We discover that users keep traveling
to diverse locations where they have not reviewed before, which is in contrast to “human
life” analogy in real life, an initial exploration followed by exploitation of existing preferences.
Interestingly, we find users who eventually leave the community show dist...[
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As Location-Based Social Networks (LBSNs) have become widely used by users, understanding
user engagement and predicting user churn are essential to the maintainability
of the services. In this thesis, we conduct a quantitative analysis to understand user engagement
patterns exhibited both offline and online in LBSNs. We employ two large-scale
datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19
million tips in Yelp and Foursquare, respectively. We discover that users keep traveling
to diverse locations where they have not reviewed before, which is in contrast to “human
life” analogy in real life, an initial exploration followed by exploitation of existing preferences.
Interestingly, we find users who eventually leave the community show distinct
engagement patterns even with their first ten reviews in various facets, e.g., geographical,
venue-specific, linguistic, and social aspects. Based on these observations, we construct
predictive models to detect potential churners. We then demonstrate the effectiveness of
our proposed features in the churn prediction. Our findings of geographical exploration
and online interactions of users enhance our understanding of human mobility based
on reviews, and provide important implications for venue recommendations and churn
prediction.
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