Geographical Engagement and Churn Prediction in Location-Based Social Networks

MPhil Thesis Defence


Title: "Geographical Engagement and Churn Prediction in Location-Based 
Social Networks"

By

Mr. Young Dae KWON


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 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.


Date:			Monday, 30 December 2019

Time:			4:00pm - 6:00pm

Venue:			Room 3494
 			Lifts 25/26

Committee Members:	Dr. Pan Hui (Supervisor)
 			Prof. Gary Chan (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****